Mammary Paget Disease - Review of the NCDB
Ramie Fathy
11/05/2019
p_table <- function(tab_data, ...) {
tab_data_2 <- deparse(substitute(tab_data))
table_p <- do.call(CreateTableOne,
list(data = as.name(tab_data_2), includeNA = TRUE, ...))
table_p_out <- print(table_p,
showAllLevels = TRUE,
printToggle = FALSE)
kable(table_p_out,
align = "c")
}uni_var <- function(test_var, data_imp) {
cat("_________________________________________________")
cat("\n")
cat(" \n##", test_var)
cat("\n")
cat("_________________________________________________")
cat("\n")
f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
as.name(test_var),
sep = " ~ " ))
data_imp_2 <- deparse(substitute(data_imp))
km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))
print(km_fit)
cat("\n")
print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
cat("\n")
cat("\n")
cat("\n")
cat(" \n## Univariable Cox Proportional Hazard Model for: ", test_var)
cat("\n")
cat("\n")
n_levels <- nlevels(data_imp[[test_var]])
if(n_levels == 1){
print("Only one level, no Cox model performed")
cat("\n")
} else {
cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))
print(summary(cox_fit))
cat("\n")
do.call("ggforest",
list(model = cox_fit, data = as.name(data_imp_2)))
}
cat("\n")
cat("\n")
cat("\n")
cat(" \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)
p <- do.call("ggsurvplot",
list(fit = km_fit, data = as.name(data_imp_2),
palette = "jco", censor = FALSE, legend = "right",
linetype = "strata", xlab = "Time (Months)"))
print(p)
}f_plot <- function(test_var, data_imp){
cat("_________________________________________________")
cat("\n")
cat(" \n##", test_var)
cat("\n")
cat("_________________________________________________")
cat("\n")
f <- as.formula(paste(as.name(test_var),
"AGE + SEX + T_SIZE + FACILITY_TYPE_F + FACILITY_LOCATION_F + YEAR_OF_DIAGNOSIS",
sep = " ~ " ))
data_imp_2 <- deparse(substitute(data_imp))
fit_fn <- do.call("glm",
list(formula = f,
data = as.name(data_imp_2),
family = "binomial"))
print(summary(fit_fn))
or <- as.data.frame(exp(coefficients(fit_fn)))
or$Variable <- rownames(or)
rownames(or) <- c()
names(or) <- c('OddsRatio', 'Variable')
ci <- as.data.frame(exp(confint(fit_fn)))
ci$Variable <- rownames(ci)
rownames(ci) <- c()
p_val_list <- tidy(fit_fn) %>%
select(term, p.value) %>%
rename(Variable = term) %>%
mutate(p.value = round(p.value, 4))
p_val_list$p.value <- as.character(p_val_list$p.value)
p_val_list$p.value[p_val_list$p.value == "0"] <- "< 0.0001"
all <- full_join(or, ci, by = 'Variable')
all <- full_join(all, p_val_list, by = "Variable")
names(all) <- c('OddsRatio', 'Variable', 'Lower', 'Upper', "p_value")
all <- na.omit(all)
all <- all %>%
filter(Variable != '(Intercept)')
text <- cbind(c("Variable", as.character(all$Variable)),
c("Odds Ratio", as.character(round(all$OddsRatio, 2))),
c("Lower CI", as.character(round(all$Lower, 2))),
c("Upper CI", as.character(round(all$Upper, 2))),
c("p Value", all$p_value))
forestplot(text,
mean = c(NA, all$OddsRatio),
lower = c(NA, all$Lower),
upper = c(NA, all$Upper),
new_page = TRUE, zero = 1,
clip = c(0.1, 100),
hrzl_lines = list("2" = gpar(col="#444444")),
vertices = TRUE,
graph.pos = 2,
xlab = "Odds Ratio (log)",
align = "c",
txt_gp = fpTxtGp(cex = 0.7),
xticks = getTicks(low = all$Lower,
high = all$Upper,
clip=c(-Inf, Inf),
exp=TRUE),
boxsize = 0.1)
}col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
1, 1, 1, 1, 1, 1, 1)
col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
"AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
"MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
"UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
"YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
"DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
"REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
"TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
"TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
"CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
"CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
"CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
"CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
"CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
"CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
"CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
"CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
"CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
"CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
"CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
"RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
"RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
"REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
"RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
"RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
"REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
"DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
"RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
"PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
"DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
"RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
"RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
"METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
"METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")
#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
NHLextr <- read_fwf('NCDBPUF_NHLExtr.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
breast <- read_fwf('NCDBPUF_Breast.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
vulva <- read_fwf('NCDBPUF_Vulva.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
vagina <- read_fwf('NCDBPUF_Vagina.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
penis <- read_fwf('NCDBPUF_Penis.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
otleuk <- read_fwf('NCDBPUF_OtLeuk.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
otheracuteleuk <- read_fwf('NCDBPUF_OtAcLeuk.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
ALL <- read_fwf('NCDBPUF_ALymLeuk.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast,
vulva, vagina, penis, NHLextr, otleuk, otheracuteleuk, ALL)
rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina,
penis, NHLextr, otleuk, otheracuteleuk, ALL)
prim_site_text <- data_frame(PRIMARY_SITE = c(
#NHL sites
"C000",
"C001",
"C002",
"C003",
"C004",
"C005",
"C006",
"C008",
"C009",
"C019",
"C020",
"C021",
"C022",
"C023",
"C024",
"C028",
"C029",
"C030",
"C031",
"C039",
"C040",
"C041",
"C048",
"C049",
"C050",
"C051",
"C052",
"C058",
"C059",
"C060",
"C061",
"C062",
"C068",
"C069",
"C079",
"C098",
"C099",
"C111",
"C142",
"C300",
"C379",
"C420",
"C422",
"C424",
#skin/melanoma
"C440", "C441", "C442", "C443", "C444", "C445",
"C446", "C447", "C448", "C449",
#breast - nipple
"C500",
#vagina/vulva
"C510", "C511", "C512", "C518", "C519", "C529",
#penis
"C600", "C601", "C602", "C608", "C609", "C639",
"C770",
"C771",
"C772",
"C773",
"C774",
"C775",
"C778",
"C779"),
SITE_TEXT = c(
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2 External Lip: NOS",
"C00.3 Lip: Upper Mucosa",
"C00.4 Lip: Lower Mucosa",
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure",
"C00.8 Lip: Overlapping",
"C00.9 Lip NOS",
"C01.9 Tongue: Base NOS",
"C02.0 Tongue: Dorsal NOS",
"C02.1 Tongue: Border, Tip",
"C02.2 Tongue: Ventral NOS",
"C02.3 Tongue: Anterior NOS",
"C02.4 Lingual Tonsil",
"C02.8 Tongue: Overlapping",
"C02.9 Tongue: NOS",
"C03.0 Gum: Upper",
"C03.1 Gum: Lower",
"C03.9 Gum NOS",
"C04.0 Mouth: Anterior Floor",
"C04.1 Mouth: Lateral Floor",
"C04.8 Mouth: Overlapping Floor",
"C04.9 Floor of Mouth NOS",
"C05.0 Hard Palate",
"C05.1 Soft Palate NOS",
"C05.2 Uvula",
"C05.8 Palate: Overlapping",
"C05.9 Palate NOS",
"C06.0 Cheek Mucosa",
"C06.1 Mouth: Vestibule",
"C06.2 Retromolar Area",
"C06.8 Mouth: Other Overlapping",
"C06.9 Mouth NOS",
"C07.9 Parotid Gland",
"C09.8 Tonsil: Overlapping",
"C09.9 Tonsil NOS",
"C11.1 Nasopharynx: Poster Wall",
"C14.2 Waldeyer Ring",
"C30.0 Nasal Cavity",
"C37.9 Thymus",
"C42.0 Blood",
"C42.2 Spleen",
"C42.4 Hematopoietic NOS",
#skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS",
#breast
"C50.0 Nipple",
#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",
#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS",
"C77.0 Lymph Nodes: HeadFaceNeck",
"C77.1 Intrathoracic Lymph Nodes",
"C77.2 Intra-abdominal LymphNodes",
"C77.3 Lymph Nodes of axilla or arm ",
"C77.4 Lymph Nodes: Leg",
"C77.5 Pelvic Lymph Nodes",
"C77.8 Lymph Nodes: multiple region",
"C77.9 Lymph Node NOS"))
dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE)
rm(prim_site_text)
# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
"DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS", "RAD_REGIONAL_DOSE_CGY",
"RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS",
"DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
"RAD_NUM_TREAT_VOL")
dat[num_vars] <- lapply(dat[num_vars], as.numeric)
# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics
dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)
dat <- dat %>%
mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
"Community Cancer Program" = "1",
"Comprehensive Comm Ca Program" = "2",
"Academic/Research Program" = "3",
"Integrated Network Ca Program" = "4",
"Other" = "9")) %>%
mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
"New England" = "1",
"Middle Atlantic" = "2",
"South Atlantic" = "3",
"East North Central" = "4",
"East South Central" = "5",
"West North Central" = "6",
"West South Central" = "7",
"Mountain" = "8",
"Pacific" = "9",
"out of US" = "0")) %>%
mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
"Northeast" = c("1", "2"),
"South" = c("3", "7"),
"Midwest" = c("4", "5", "6"),
"West" = c("8", "9"))) %>%
mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
mutate(SEX_F = fct_recode(SEX,
"Male" = "1",
"Female" = "2")) %>%
mutate(RACE_F = fct_collapse(RACE,
"White" = c("01"),
"Black" = c("02"),
"Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
"16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
"32", "96", "97"),
"Other/Unk" = c("03", "98", "99"))) %>%
mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
"Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
"No" = c("0"),
"Unknown" = c("9"))) %>%
mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
"None" = "0",
"Private" = "1",
"Medicaid" = "2",
"Medicare" = "3",
"Other Government" = "4",
"Unknown" = "9")) %>%
mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
"Private")) %>%
mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
"Less than $38,000" = "1",
"$38,000 - $47,999" = "2",
"$48,000 - $62,999" = "3",
"$63,000 +" = "4")) %>%
mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
"21% or more" = "1",
"13 - 20.9%" = "2",
"7 - 12.9%" = "3",
"Less than 7%" = "4")) %>%
mutate(U_R_F = fct_collapse(UR_CD_13,
"Metro" = c("1", "2", "3"),
"Urban" = c("4", "5", "6", "7"),
"Rural" = c("8", "9"))) %>%
mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
All_Part_Prim = c("10", "11", "12", "13",
"14", "20", "21", "22"),
Other_Facility = c("00"))) %>%
mutate(GRADE_F = fct_recode(GRADE,
"Gr I: Well Diff" = "1",
"Gr II: Mod Diff" = "2",
"Gr III: Poor Diff" = "3",
"Gr IV: Undiff/Anaplastic" = "4",
"NA/Unkown" = "9")) %>%
mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
"N_A" = "88")) %>%
mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
"1")) %>%
mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
"N_A" = "88")) %>%
mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
"N_A" = "88")) %>%
mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
"N_A" = "88")) %>%
mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
"1")) %>%
mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
"N_A" = "88")) %>%
mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
"N_A" = "88")) %>%
mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
"N_A" = "88")) %>%
mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
"N_A" = "88")) %>%
mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
"No Residual" = "0",
"Residual, NOS" = "1",
"Microscopic Resid" = "2",
"Macroscopic Resid" = "3",
"Not evaluable" = "7",
"No surg" = "8",
"Unknown" = "9")) %>%
mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
"Yes" = c("1", "2", "3"),
"No" = c("0"),
"No surg/Unk/NA" = c("7", "8", "9"))) %>%
mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
"No_Surg_or_No_Readmit" = "0",
"Unplan_Readmit_Same" = "1",
"Plan_Readmit_Same" = "2",
"PlanUnplan_Same" = "3",
"Unknown" = "4")) %>%
mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
"None" = "0",
"Beam Radiation" = "1",
"Radioactive Implants" = "2",
"Radioisotopes" = "3",
"Beam + Imp or Isotopes" = "4",
"Radiation, NOS" = "5",
"Unknown" = "9")) %>%
mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
"Alive_30" = "0",
"Dead_30" = "1",
"Unknown" = "9")) %>%
mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
"Alive_90" = "0",
"Dead_90" = "1",
"Unknown" = "9")) %>%
mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
"Neg_LymphVasc_Inv" = "0",
"Pos_LumphVasc_Inv" = "1",
"N_A" = "8",
"Unknown" = "9")) %>%
mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
"No_Surg" = "0",
"Robot_Assist" = "1",
"Robot_to_Open" = "2",
"Endo_Lap" = "3",
"Endo_Lap_to_Open" = "4",
"Open_Unknown" = "5",
"Unknown" = "9")) %>%
mutate(All = "All") %>%
mutate(All = factor(All)) %>%
mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
"Surg performed" = "0",
"Surg not recommended" = "1",
"No surg due to pt factors" = "2",
"No surg, pt died" = "5",
"Surg rec, not done" = "6",
"Surg rec, pt refused" = "7",
"Surg rec, unk if done" = "8",
"Unknown" = "9")) %>%
mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
"Yes",
ifelse(REASON_FOR_NO_SURGERY == "9",
"Ukn",
"No"))) %>%
mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
SURGERY_YN == "No" ~ FALSE,
TRUE ~ NA)) %>%
mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
"Rad performed" = "0",
"Rad not recommended" = "1",
"No Rad due to pt factors" = "2",
"No Rad, pt died" = "5",
"Rad rec, not done" = "6",
"Rad rec, pt refused" = "7",
"Rad rec, unk if done" = "8",
"Unknown" = "9")) %>%
mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
"Yes",
ifelse(REASON_FOR_NO_RADIATION == "9",
NA,
"No"))) %>%
mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
"None or Surg or Rad" = "0",
"Rad before Surg" = "2",
"Surg before Rad" = "3",
"Rad before and after Surg" = "4",
"Intraop Rad" = "5",
"Intraop Rad plus other" = "6",
"Unknown" = "9")) %>%
mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
"Surg Alone",
ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
"Rad Alone",
ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
"No Treatment",
ifelse(RX_SUMM_SURGRAD_SEQ == "2",
"Rad then Surg",
ifelse(RX_SUMM_SURGRAD_SEQ == "3",
"Surg then Rad",
ifelse(RX_SUMM_SURGRAD_SEQ == "4",
"Rad before and after Surg",
"Other"))))))) %>%
mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
"Surg Alone",
"Surg then Rad",
"Rad Alone")) %>%
mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
"No" = c("00", "82", "85", "86", "87"),
"Yes" = c("01", "02", "03"),
"Ukn" = c("88", "99"))) %>%
mutate(IMMUNO_YN = fct_collapse(RX_SUMM_IMMUNOTHERAPY,
"No" = c("00", "82", "85", "86", "87"),
"Yes" = c("01"),
"Ukn" = c("88", "99"))) %>%
mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
"Surg, No rad, No Chemo",
ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
"Rad, No Surg, No Chemo",
ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
"No Surg, No Rad, No Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
"Rad then Surg, No Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
"Surg then Rad, No Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
"Rad before and after Surg, No Chemo",
ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
"Surg, No rad, Yes Chemo",
ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
"Rad, No Surg, Yes Chemo",
ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
"No Surg, No Rad, Yes Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
"Rad then Surg, Yes Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
"Surg then Rad, Yes Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
"Rad before and after Surg, Yes Chemo",
"Other"))))))))))))) %>%
mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
mutate(T_SIZE = ifelse(T_SIZE == 0,
"No Tumor",
ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
"< 1 cm",
ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
"1-2 cm",
ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
"2-3 cm",
ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
"3-4 cm",
ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
"4-5 cm",
ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
"5-6 cm",
ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
T_SIZE == 980 | T_SIZE == 989 |
T_SIZE == 997,
">6 cm",
ifelse(T_SIZE == 988 | T_SIZE == 999,
"NA_unk",
"Microscopic focus")))))))))) %>%
mutate(T_SIZE = factor(T_SIZE)) %>%
mutate(T_SIZE = fct_relevel(T_SIZE,
"No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
"4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
CS_METS_DX_BONE == "1" ~ "Bone",
CS_METS_DX_BRAIN == "1" ~ "Brain",
CS_METS_DX_LIVER == "1" ~ "Liver",
TRUE ~ "None/Other/Unk/NA")) %>%
mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
"Non-Expansion State" = "0",
"Jan 2014 Expansion States" = "1",
"Early Expansion States (2010-13)" = "2",
"Late Expansion States (> Jan 2014)" = "3",
"Suppressed for Ages 0 - 39" = "9")) %>%
mutate(EXPN_GROUP = case_when(MEDICAID_EXPN_CODE %in% c("Jan 2014 Expansion States") &
YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
MEDICAID_EXPN_CODE %in% c("Jan 2014 Expansion States") &
YEAR_OF_DIAGNOSIS %in%
c("2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
MEDICAID_EXPN_CODE %in% c("Early Expansion States (2010-13)") &
YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
MEDICAID_EXPN_CODE %in% c("Early Expansion States (2010-13)") &
YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",
MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",
MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") &
YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013")) %>%
mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>%
mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No" &
IMMUNO_YN == "No", FALSE,
ifelse(CHEMO_YN == "Ukn", NA,
TRUE)))
fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
"HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
"CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
"BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
"TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
"MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
"PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")
dat <- dat %>%
mutate_at(fact_vars_2, funs(factor(.)))Extract data of interest
# MPD
site_code <- c(
#breast
"C500", "C501", "C502","C503","C504","C505",
"C506","C508","C509")
histo_code <- c("8540")
behavior_code <- c("3")
data <- dat %>%
filter(BEHAVIOR %in% behavior_code) %>%
filter(PRIMARY_SITE %in% site_code) %>%
filter(HISTOLOGY %in% histo_code) %>%
# filter(AGE >= 18) %>%
filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
filter(SEQUENCE_NUMBER == "00")
no_Excludes <- as.data.frame(data %>%
filter(EXPN_GROUP != "Exclude")
%>% droplevels())
#file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
#save(data,
# file = paste0(file_path, "/breast_data.Rda"))#load("EMPD_data.Rda")Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:
- Site codes: C500, C501, C502, C503, C504, C505, C506, C508, C509
- Histology codes: 8540
- Behavior codes: 3
Patients were excluded if they didn’t have values for either follow up or vital status.
Patients were excluded if they had surgery to a distant site using RX_SUMM_SURG_OTH_REGDIS. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed.
data %>%
CreateTableOne(data = .,
vars = c("RX_SUMM_SURG_OTH_REGDIS"),
includeNA = TRUE) %>%
print(.,
showAllLevels = TRUE)
level Overall
n 700
RX_SUMM_SURG_OTH_REGDIS (%) 0 675 (96.4)
1 6 ( 0.9)
2 1 ( 0.1)
3 0 ( 0.0)
4 3 ( 0.4)
5 0 ( 0.0)
9 15 ( 2.1) data <- data %>%
filter(RX_SUMM_SURG_OTH_REGDIS == "0") Race was grouped as white, black, asian, other/unknown Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed Whether surgery was performed was based on the REASON_FOR_NO_SURGERY variable. The SURGERY_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.
Whether radiation was performed was based on the REASON_FOR_NO_RADIATION variable. The RADIATION_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.
##Table of variables for all cases:
p_table(data,
vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY", "AGE", "AGE_F", "AGE_40",
"SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F",
"INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
"SITE_TEXT", "BEHAVIOR", "GRADE_F",
"DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
"TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
"TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
"DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
"READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
"PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS",
"LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
"SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN",
"IMMUNO_YN", "Tx_YN", "mets_at_dx",
"MEDICAID_EXPN_CODE", "EXPN_GROUP"))| level | Overall | |
|---|---|---|
| n | 675 | |
| FACILITY_TYPE_F (%) | Community Cancer Program | 81 ( 12.0) |
| Comprehensive Comm Ca Program | 296 ( 43.9) | |
| Academic/Research Program | 173 ( 25.6) | |
| Integrated Network Ca Program | 90 ( 13.3) | |
| NA | 35 ( 5.2) | |
| FACILITY_LOCATION_F (%) | New England | 31 ( 4.6) |
| Middle Atlantic | 93 ( 13.8) | |
| South Atlantic | 143 ( 21.2) | |
| East North Central | 134 ( 19.9) | |
| East South Central | 44 ( 6.5) | |
| West North Central | 59 ( 8.7) | |
| West South Central | 60 ( 8.9) | |
| Mountain | 35 ( 5.2) | |
| Pacific | 41 ( 6.1) | |
| NA | 35 ( 5.2) | |
| FACILITY_GEOGRAPHY (%) | Northeast | 124 ( 18.4) |
| South | 203 ( 30.1) | |
| Midwest | 237 ( 35.1) | |
| West | 76 ( 11.3) | |
| NA | 35 ( 5.2) | |
| AGE (mean (sd)) | 65.56 (15.04) | |
| AGE_F (%) | (0,54] | 161 ( 23.9) |
| (54,64] | 149 ( 22.1) | |
| (64,74] | 149 ( 22.1) | |
| (74,100] | 216 ( 32.0) | |
| AGE_40 (%) | (0,40] | 38 ( 5.6) |
| (40,100] | 637 ( 94.4) | |
| SEX_F (%) | Male | 19 ( 2.8) |
| Female | 656 ( 97.2) | |
| RACE_F (%) | White | 570 ( 84.4) |
| Black | 77 ( 11.4) | |
| Other/Unk | 16 ( 2.4) | |
| Asian | 12 ( 1.8) | |
| HISPANIC (%) | No | 587 ( 87.0) |
| Yes | 27 ( 4.0) | |
| Unknown | 61 ( 9.0) | |
| INSURANCE_F (%) | Private | 273 ( 40.4) |
| None | 24 ( 3.6) | |
| Medicaid | 35 ( 5.2) | |
| Medicare | 320 ( 47.4) | |
| Other Government | 8 ( 1.2) | |
| Unknown | 15 ( 2.2) | |
| INCOME_F (%) | Less than $38,000 | 130 ( 19.3) |
| $38,000 - $47,999 | 144 ( 21.3) | |
| $48,000 - $62,999 | 178 ( 26.4) | |
| $63,000 + | 218 ( 32.3) | |
| NA | 5 ( 0.7) | |
| EDUCATION_F (%) | 21% or more | 99 ( 14.7) |
| 13 - 20.9% | 164 ( 24.3) | |
| 7 - 12.9% | 222 ( 32.9) | |
| Less than 7% | 186 ( 27.6) | |
| NA | 4 ( 0.6) | |
| U_R_F (%) | Metro | 557 ( 82.5) |
| Urban | 88 ( 13.0) | |
| Rural | 12 ( 1.8) | |
| NA | 18 ( 2.7) | |
| CROWFLY (mean (sd)) | 25.98 (113.56) | |
| CDCC_TOTAL_BEST (%) | 0 | 558 ( 82.7) |
| 1 | 80 ( 11.9) | |
| 2 | 23 ( 3.4) | |
| 3 | 14 ( 2.1) | |
| SITE_TEXT (%) | C00.0 External Lip: Upper NOS | 0 ( 0.0) |
| C00.1 External Lip: Lower NOS | 0 ( 0.0) | |
| C00.2 External Lip: NOS | 0 ( 0.0) | |
| C00.3 Lip: Upper Mucosa | 0 ( 0.0) | |
| C00.4 Lip: Lower Mucosa | 0 ( 0.0) | |
| C00.5 Lip: Mucosa NOS | 0 ( 0.0) | |
| C00.6 Lip: Commissure | 0 ( 0.0) | |
| C00.8 Lip: Overlapping | 0 ( 0.0) | |
| C00.9 Lip NOS | 0 ( 0.0) | |
| C01.9 Tongue: Base NOS | 0 ( 0.0) | |
| C02.0 Tongue: Dorsal NOS | 0 ( 0.0) | |
| C02.1 Tongue: Border, Tip | 0 ( 0.0) | |
| C02.2 Tongue: Ventral NOS | 0 ( 0.0) | |
| C02.3 Tongue: Anterior NOS | 0 ( 0.0) | |
| C02.4 Lingual Tonsil | 0 ( 0.0) | |
| C02.8 Tongue: Overlapping | 0 ( 0.0) | |
| C02.9 Tongue: NOS | 0 ( 0.0) | |
| C03.0 Gum: Upper | 0 ( 0.0) | |
| C03.1 Gum: Lower | 0 ( 0.0) | |
| C03.9 Gum NOS | 0 ( 0.0) | |
| C04.0 Mouth: Anterior Floor | 0 ( 0.0) | |
| C04.1 Mouth: Lateral Floor | 0 ( 0.0) | |
| C04.9 Floor of Mouth NOS | 0 ( 0.0) | |
| C05.0 Hard Palate | 0 ( 0.0) | |
| C05.1 Soft Palate NOS | 0 ( 0.0) | |
| C05.2 Uvula | 0 ( 0.0) | |
| C05.8 Palate: Overlapping | 0 ( 0.0) | |
| C05.9 Palate NOS | 0 ( 0.0) | |
| C06.0 Cheek Mucosa | 0 ( 0.0) | |
| C06.1 Mouth: Vestibule | 0 ( 0.0) | |
| C06.2 Retromolar Area | 0 ( 0.0) | |
| C06.8 Mouth: Other Overlapping | 0 ( 0.0) | |
| C06.9 Mouth NOS | 0 ( 0.0) | |
| C07.9 Parotid Gland | 0 ( 0.0) | |
| C09.8 Tonsil: Overlapping | 0 ( 0.0) | |
| C09.9 Tonsil NOS | 0 ( 0.0) | |
| C11.1 Nasopharynx: Poster Wall | 0 ( 0.0) | |
| C14.2 Waldeyer Ring | 0 ( 0.0) | |
| C30.0 Nasal Cavity | 0 ( 0.0) | |
| C37.9 Thymus | 0 ( 0.0) | |
| C42.0 Blood | 0 ( 0.0) | |
| C42.2 Spleen | 0 ( 0.0) | |
| C42.4 Hematopoietic NOS | 0 ( 0.0) | |
| C44.0 Skin of lip, NOS | 0 ( 0.0) | |
| C44.1 Eyelid | 0 ( 0.0) | |
| C44.2 External ear | 0 ( 0.0) | |
| C44.3 Skin of ear and unspecified parts of face | 0 ( 0.0) | |
| C44.4 Skin of scalp and neck | 0 ( 0.0) | |
| C44.5 Skin of trunk | 0 ( 0.0) | |
| C44.6 Skin of upper limb and shoulder | 0 ( 0.0) | |
| C44.7 Skin of lower limb and hip | 0 ( 0.0) | |
| C44.8 Overlapping lesion of skin | 0 ( 0.0) | |
| C44.9 Skin, NOS | 0 ( 0.0) | |
| C50.0 Nipple | 448 ( 66.4) | |
| C51.0 Labium majus | 0 ( 0.0) | |
| C51.1 Labium minus | 0 ( 0.0) | |
| C51.2 Clitoris | 0 ( 0.0) | |
| C51.8 Overlapping lesion of vulva | 0 ( 0.0) | |
| C51.9 Vulva, NOS | 0 ( 0.0) | |
| C52.9 Vagina, NOS | 0 ( 0.0) | |
| C60.0 Prepuce | 0 ( 0.0) | |
| C60.1 Glans penis | 0 ( 0.0) | |
| C60.2 Body of penis | 0 ( 0.0) | |
| C60.8 Overlapping lesion of penis | 0 ( 0.0) | |
| C60.9 Penis | 0 ( 0.0) | |
| C63.2 Scrotum, NOS | 0 ( 0.0) | |
| C77.0 Lymph Nodes: HeadFaceNeck | 0 ( 0.0) | |
| C77.1 Intrathoracic Lymph Nodes | 0 ( 0.0) | |
| C77.2 Intra-abdominal LymphNodes | 0 ( 0.0) | |
| C77.3 Lymph Nodes of axilla or arm | 0 ( 0.0) | |
| C77.4 Lymph Nodes: Leg | 0 ( 0.0) | |
| C77.5 Pelvic Lymph Nodes | 0 ( 0.0) | |
| C77.8 Lymph Nodes: multiple region | 0 ( 0.0) | |
| C77.9 Lymph Node NOS | 0 ( 0.0) | |
| NA | 227 ( 33.6) | |
| BEHAVIOR (%) | 2 | 0 ( 0.0) |
| 3 | 675 (100.0) | |
| GRADE_F (%) | Gr I: Well Diff | 22 ( 3.3) |
| Gr II: Mod Diff | 55 ( 8.1) | |
| Gr III: Poor Diff | 106 ( 15.7) | |
| Gr IV: Undiff/Anaplastic | 2 ( 0.3) | |
| 5 | 0 ( 0.0) | |
| 6 | 0 ( 0.0) | |
| 7 | 0 ( 0.0) | |
| 8 | 0 ( 0.0) | |
| NA/Unkown | 490 ( 72.6) | |
| DX_STAGING_PROC_DAYS (mean (sd)) | 1.68 (10.24) | |
| TNM_CLIN_T (%) | N_A | 0 ( 0.0) |
| c0 | 5 ( 0.7) | |
| c1 | 37 ( 5.5) | |
| c1A | 12 ( 1.8) | |
| c1B | 9 ( 1.3) | |
| c1C | 15 ( 2.2) | |
| c1MI | 4 ( 0.6) | |
| c2 | 21 ( 3.1) | |
| c2A | 0 ( 0.0) | |
| c2B | 0 ( 0.0) | |
| c2C | 0 ( 0.0) | |
| c2D | 0 ( 0.0) | |
| c3 | 18 ( 2.7) | |
| c3A | 0 ( 0.0) | |
| c3B | 0 ( 0.0) | |
| c4 | 10 ( 1.5) | |
| c4A | 0 ( 0.0) | |
| c4B | 10 ( 1.5) | |
| c4C | 0 ( 0.0) | |
| c4D | 3 ( 0.4) | |
| cX | 188 ( 27.9) | |
| pA | 0 ( 0.0) | |
| pIS | 307 ( 45.5) | |
| NA | 36 ( 5.3) | |
| TNM_CLIN_N (%) | N_A | 0 ( 0.0) |
| c0 | 449 ( 66.5) | |
| c1 | 26 ( 3.9) | |
| c1A | 0 ( 0.0) | |
| c1B | 0 ( 0.0) | |
| c2 | 6 ( 0.9) | |
| c2A | 2 ( 0.3) | |
| c2B | 1 ( 0.1) | |
| c2C | 0 ( 0.0) | |
| c3 | 3 ( 0.4) | |
| c3A | 1 ( 0.1) | |
| c3B | 0 ( 0.0) | |
| c3C | 0 ( 0.0) | |
| c4 | 0 ( 0.0) | |
| cX | 162 ( 24.0) | |
| NA | 25 ( 3.7) | |
| TNM_CLIN_M (%) | N_A | 0 ( 0.0) |
| c0 | 614 ( 91.0) | |
| c0I+ | 0 ( 0.0) | |
| c1 | 26 ( 3.9) | |
| c1A | 0 ( 0.0) | |
| c1B | 0 ( 0.0) | |
| c1C | 0 ( 0.0) | |
| NA | 35 ( 5.2) | |
| TNM_CLIN_STAGE_GROUP (%) | 0 | 333 ( 49.3) |
| 1 | 39 ( 5.8) | |
| 1A | 32 ( 4.7) | |
| 1B | 1 ( 0.1) | |
| 1C | 0 ( 0.0) | |
| 2 | 0 ( 0.0) | |
| 2A | 23 ( 3.4) | |
| 2B | 13 ( 1.9) | |
| 2C | 0 ( 0.0) | |
| 3 | 2 ( 0.3) | |
| 3A | 7 ( 1.0) | |
| 3B | 12 ( 1.8) | |
| 3C | 3 ( 0.4) | |
| 4 | 27 ( 4.0) | |
| 4A | 0 ( 0.0) | |
| 4A1 | 0 ( 0.0) | |
| 4A2 | 0 ( 0.0) | |
| 4B | 0 ( 0.0) | |
| 4C | 0 ( 0.0) | |
| N_A | 0 ( 0.0) | |
| 99 | 183 ( 27.1) | |
| TNM_PATH_T (%) | N_A | 0 ( 0.0) |
| p0 | 8 ( 1.2) | |
| p1 | 13 ( 1.9) | |
| p1A | 17 ( 2.5) | |
| p1B | 11 ( 1.6) | |
| p1C | 24 ( 3.6) | |
| p1MI | 7 ( 1.0) | |
| p2 | 12 ( 1.8) | |
| p2A | 0 ( 0.0) | |
| p2B | 0 ( 0.0) | |
| p2C | 0 ( 0.0) | |
| p2D | 0 ( 0.0) | |
| p3 | 2 ( 0.3) | |
| p3A | 0 ( 0.0) | |
| p3B | 0 ( 0.0) | |
| p4 | 1 ( 0.1) | |
| p4A | 0 ( 0.0) | |
| p4B | 5 ( 0.7) | |
| p4C | 0 ( 0.0) | |
| p4D | 3 ( 0.4) | |
| pA | 0 ( 0.0) | |
| pIS | 261 ( 38.7) | |
| pX | 253 ( 37.5) | |
| NA | 58 ( 8.6) | |
| TNM_PATH_N (%) | N_A | 0 ( 0.0) |
| p0 | 233 ( 34.5) | |
| p0I- | 30 ( 4.4) | |
| p0I+ | 2 ( 0.3) | |
| p0M- | 0 ( 0.0) | |
| p0M+ | 0 ( 0.0) | |
| p1 | 7 ( 1.0) | |
| p1A | 9 ( 1.3) | |
| p1B | 0 ( 0.0) | |
| p1C | 0 ( 0.0) | |
| p1MI | 2 ( 0.3) | |
| p2 | 2 ( 0.3) | |
| p2A | 2 ( 0.3) | |
| p2B | 0 ( 0.0) | |
| p2C | 0 ( 0.0) | |
| p3 | 2 ( 0.3) | |
| p3A | 3 ( 0.4) | |
| p3B | 0 ( 0.0) | |
| p3C | 0 ( 0.0) | |
| p4 | 0 ( 0.0) | |
| pX | 304 ( 45.0) | |
| NA | 79 ( 11.7) | |
| TNM_PATH_M (%) | N_A | 0 ( 0.0) |
| p0 | 0 ( 0.0) | |
| p1 | 8 ( 1.2) | |
| p1A | 0 ( 0.0) | |
| p1B | 0 ( 0.0) | |
| p1C | 0 ( 0.0) | |
| pX | 344 ( 51.0) | |
| NA | 323 ( 47.9) | |
| TNM_PATH_STAGE_GROUP (%) | 0 | 298 ( 44.1) |
| 1 | 55 ( 8.1) | |
| 1A | 26 ( 3.9) | |
| 1B | 2 ( 0.3) | |
| 1C | 0 ( 0.0) | |
| 2 | 3 ( 0.4) | |
| 2A | 21 ( 3.1) | |
| 2B | 8 ( 1.2) | |
| 2C | 0 ( 0.0) | |
| 3 | 0 ( 0.0) | |
| 3A | 4 ( 0.6) | |
| 3B | 6 ( 0.9) | |
| 3C | 5 ( 0.7) | |
| 4 | 9 ( 1.3) | |
| 4A | 0 ( 0.0) | |
| 4A1 | 0 ( 0.0) | |
| 4B | 0 ( 0.0) | |
| 4C | 0 ( 0.0) | |
| N_A | 0 ( 0.0) | |
| 99 | 198 ( 29.3) | |
| NA | 40 ( 5.9) | |
| DX_RX_STARTED_DAYS (mean (sd)) | 35.43 (34.86) | |
| DX_SURG_STARTED_DAYS (mean (sd)) | 38.84 (40.14) | |
| DX_DEFSURG_STARTED_DAYS (mean (sd)) | 46.51 (46.12) | |
| MARGINS (%) | No Residual | 511 ( 75.7) |
| Residual, NOS | 10 ( 1.5) | |
| Microscopic Resid | 11 ( 1.6) | |
| Macroscopic Resid | 3 ( 0.4) | |
| Not evaluable | 0 ( 0.0) | |
| No surg | 122 ( 18.1) | |
| Unknown | 18 ( 2.7) | |
| MARGINS_YN (%) | No | 511 ( 75.7) |
| Yes | 24 ( 3.6) | |
| No surg/Unk/NA | 140 ( 20.7) | |
| SURG_DISCHARGE_DAYS (mean (sd)) | 1.00 (4.05) | |
| READM_HOSP_30_DAYS_F (%) | No_Surg_or_No_Readmit | 627 ( 92.9) |
| Unplan_Readmit_Same | 15 ( 2.2) | |
| Plan_Readmit_Same | 18 ( 2.7) | |
| PlanUnplan_Same | 1 ( 0.1) | |
| 9 | 14 ( 2.1) | |
| RX_SUMM_RADIATION_F (%) | None | 465 ( 68.9) |
| Beam Radiation | 200 ( 29.6) | |
| Radioactive Implants | 2 ( 0.3) | |
| Radioisotopes | 0 ( 0.0) | |
| Beam + Imp or Isotopes | 0 ( 0.0) | |
| Radiation, NOS | 0 ( 0.0) | |
| Unknown | 8 ( 1.2) | |
| PUF_30_DAY_MORT_CD_F (%) | Alive_30 | 542 ( 80.3) |
| Dead_30 | 1 ( 0.1) | |
| Unknown | 6 ( 0.9) | |
| NA | 126 ( 18.7) | |
| PUF_90_DAY_MORT_CD_F (%) | Alive_90 | 536 ( 79.4) |
| Dead_90 | 3 ( 0.4) | |
| Unknown | 10 ( 1.5) | |
| NA | 126 ( 18.7) | |
| DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) | 59.19 (42.79) | |
| LYMPH_VASCULAR_INVASION_F (%) | Neg_LymphVasc_Inv | 162 ( 24.0) |
| Pos_LumphVasc_Inv | 10 ( 1.5) | |
| N_A | 0 ( 0.0) | |
| Unknown | 154 ( 22.8) | |
| NA | 349 ( 51.7) | |
| RX_HOSP_SURG_APPR_2010_F (%) | No_Surg | 96 ( 14.2) |
| Robot_Assist | 0 ( 0.0) | |
| Robot_to_Open | 0 ( 0.0) | |
| Endo_Lap | 1 ( 0.1) | |
| Endo_Lap_to_Open | 0 ( 0.0) | |
| Open_Unknown | 229 ( 33.9) | |
| Unknown | 0 ( 0.0) | |
| NA | 349 ( 51.7) | |
| SURG_RAD_SEQ (%) | Surg Alone | 363 ( 53.8) |
| Surg then Rad | 181 ( 26.8) | |
| Rad Alone | 19 ( 2.8) | |
| No Treatment | 95 ( 14.1) | |
| Other | 15 ( 2.2) | |
| Rad before and after Surg | 0 ( 0.0) | |
| Rad then Surg | 2 ( 0.3) | |
| SURG_RAD_SEQ_C (%) | Surg, No rad, No Chemo | 321 ( 47.6) |
| Surg then Rad, No Chemo | 141 ( 20.9) | |
| Surg then Rad, Yes Chemo | 33 ( 4.9) | |
| Surg, No rad, Yes Chemo | 25 ( 3.7) | |
| No Surg, No Rad, Yes Chemo | 9 ( 1.3) | |
| No Surg, No Rad, No Chemo | 81 ( 12.0) | |
| Other | 46 ( 6.8) | |
| Rad, No Surg, Yes Chemo | 5 ( 0.7) | |
| Rad, No Surg, No Chemo | 12 ( 1.8) | |
| Rad then Surg, Yes Chemo | 2 ( 0.3) | |
| Rad then Surg, No Chemo | 0 ( 0.0) | |
| Rad before and after Surg, Yes Chemo | 0 ( 0.0) | |
| Rad before and after Surg, No Chemo | 0 ( 0.0) | |
| SURGERY_YN (%) | No | 115 ( 17.0) |
| Ukn | 9 ( 1.3) | |
| Yes | 551 ( 81.6) | |
| RADIATION_YN (%) | No | 466 ( 69.0) |
| Yes | 202 ( 29.9) | |
| NA | 7 ( 1.0) | |
| CHEMO_YN (%) | No | 563 ( 83.4) |
| Yes | 74 ( 11.0) | |
| Ukn | 38 ( 5.6) | |
| IMMUNO_YN (%) | No | 657 ( 97.3) |
| Yes | 5 ( 0.7) | |
| Ukn | 13 ( 1.9) | |
| Tx_YN (%) | FALSE | 80 ( 11.9) |
| TRUE | 557 ( 82.5) | |
| NA | 38 ( 5.6) | |
| mets_at_dx (%) | Bone | 10 ( 1.5) |
| Brain | 0 ( 0.0) | |
| Liver | 1 ( 0.1) | |
| Lung | 4 ( 0.6) | |
| None/Other/Unk/NA | 660 ( 97.8) | |
| MEDICAID_EXPN_CODE (%) | Non-Expansion State | 261 ( 38.7) |
| Jan 2014 Expansion States | 207 ( 30.7) | |
| Early Expansion States (2010-13) | 77 ( 11.4) | |
| Late Expansion States (> Jan 2014) | 95 ( 14.1) | |
| Suppressed for Ages 0 - 39 | 35 ( 5.2) | |
| EXPN_GROUP (%) | Exclude | 35 ( 5.2) |
| Post-Expansion | 62 ( 9.2) | |
| Pre-Expansion | 578 ( 85.6) |
p_table(data,
vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY", "AGE", "AGE_F", "AGE_40",
"SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F",
"INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
"SITE_TEXT", "BEHAVIOR", "GRADE_F",
"DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
"TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
"TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
"DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
"READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
"PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS",
"LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
"SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN",
"IMMUNO_YN", "Tx_YN", "mets_at_dx",
"MEDICAID_EXPN_CODE"),
strata = "SURGERY_YN")no non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -InfVariable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.
| level | No | Ukn | Yes | p | test | |
|---|---|---|---|---|---|---|
| n | 115 | 9 | 551 | |||
| FACILITY_TYPE_F (%) | Community Cancer Program | 18 ( 15.7) | 3 ( 33.3) | 60 ( 10.9) | 0.279 | |
| Comprehensive Comm Ca Program | 49 ( 42.6) | 3 ( 33.3) | 244 ( 44.3) | |||
| Academic/Research Program | 32 ( 27.8) | 2 ( 22.2) | 139 ( 25.2) | |||
| Integrated Network Ca Program | 14 ( 12.2) | 1 ( 11.1) | 75 ( 13.6) | |||
| NA | 2 ( 1.7) | 0 ( 0.0) | 33 ( 6.0) | |||
| FACILITY_LOCATION_F (%) | New England | 10 ( 8.7) | 2 ( 22.2) | 19 ( 3.4) | 0.008 | |
| Middle Atlantic | 23 ( 20.0) | 3 ( 33.3) | 67 ( 12.2) | |||
| South Atlantic | 23 ( 20.0) | 0 ( 0.0) | 120 ( 21.8) | |||
| East North Central | 21 ( 18.3) | 1 ( 11.1) | 112 ( 20.3) | |||
| East South Central | 7 ( 6.1) | 0 ( 0.0) | 37 ( 6.7) | |||
| West North Central | 9 ( 7.8) | 1 ( 11.1) | 49 ( 8.9) | |||
| West South Central | 15 ( 13.0) | 1 ( 11.1) | 44 ( 8.0) | |||
| Mountain | 2 ( 1.7) | 0 ( 0.0) | 33 ( 6.0) | |||
| Pacific | 3 ( 2.6) | 1 ( 11.1) | 37 ( 6.7) | |||
| NA | 2 ( 1.7) | 0 ( 0.0) | 33 ( 6.0) | |||
| FACILITY_GEOGRAPHY (%) | Northeast | 33 ( 28.7) | 5 ( 55.6) | 86 ( 15.6) | 0.001 | |
| South | 38 ( 33.0) | 1 ( 11.1) | 164 ( 29.8) | |||
| Midwest | 37 ( 32.2) | 2 ( 22.2) | 198 ( 35.9) | |||
| West | 5 ( 4.3) | 1 ( 11.1) | 70 ( 12.7) | |||
| NA | 2 ( 1.7) | 0 ( 0.0) | 33 ( 6.0) | |||
| AGE (mean (sd)) | 70.50 (15.48) | 73.44 (12.27) | 64.40 (14.76) | <0.001 | ||
| AGE_F (%) | (0,54] | 20 ( 17.4) | 0 ( 0.0) | 141 ( 25.6) | 0.003 | |
| (54,64] | 27 ( 23.5) | 3 ( 33.3) | 119 ( 21.6) | |||
| (64,74] | 15 ( 13.0) | 2 ( 22.2) | 132 ( 24.0) | |||
| (74,100] | 53 ( 46.1) | 4 ( 44.4) | 159 ( 28.9) | |||
| AGE_40 (%) | (0,40] | 3 ( 2.6) | 0 ( 0.0) | 35 ( 6.4) | 0.217 | |
| (40,100] | 112 ( 97.4) | 9 (100.0) | 516 ( 93.6) | |||
| SEX_F (%) | Male | 2 ( 1.7) | 0 ( 0.0) | 17 ( 3.1) | 0.639 | |
| Female | 113 ( 98.3) | 9 (100.0) | 534 ( 96.9) | |||
| RACE_F (%) | White | 89 ( 77.4) | 7 ( 77.8) | 474 ( 86.0) | 0.280 | |
| Black | 20 ( 17.4) | 2 ( 22.2) | 55 ( 10.0) | |||
| Other/Unk | 4 ( 3.5) | 0 ( 0.0) | 12 ( 2.2) | |||
| Asian | 2 ( 1.7) | 0 ( 0.0) | 10 ( 1.8) | |||
| HISPANIC (%) | No | 102 ( 88.7) | 9 (100.0) | 476 ( 86.4) | 0.262 | |
| Yes | 7 ( 6.1) | 0 ( 0.0) | 20 ( 3.6) | |||
| Unknown | 6 ( 5.2) | 0 ( 0.0) | 55 ( 10.0) | |||
| INSURANCE_F (%) | Private | 32 ( 27.8) | 3 ( 33.3) | 238 ( 43.2) | 0.014 | |
| None | 8 ( 7.0) | 0 ( 0.0) | 16 ( 2.9) | |||
| Medicaid | 9 ( 7.8) | 1 ( 11.1) | 25 ( 4.5) | |||
| Medicare | 61 ( 53.0) | 4 ( 44.4) | 255 ( 46.3) | |||
| Other Government | 0 ( 0.0) | 0 ( 0.0) | 8 ( 1.5) | |||
| Unknown | 5 ( 4.3) | 1 ( 11.1) | 9 ( 1.6) | |||
| INCOME_F (%) | Less than $38,000 | 29 ( 25.2) | 2 ( 22.2) | 99 ( 18.0) | 0.277 | |
| $38,000 - $47,999 | 22 ( 19.1) | 0 ( 0.0) | 122 ( 22.1) | |||
| $48,000 - $62,999 | 32 ( 27.8) | 4 ( 44.4) | 142 ( 25.8) | |||
| $63,000 + | 30 ( 26.1) | 3 ( 33.3) | 185 ( 33.6) | |||
| NA | 2 ( 1.7) | 0 ( 0.0) | 3 ( 0.5) | |||
| EDUCATION_F (%) | 21% or more | 28 ( 24.3) | 2 ( 22.2) | 69 ( 12.5) | 0.048 | |
| 13 - 20.9% | 26 ( 22.6) | 2 ( 22.2) | 136 ( 24.7) | |||
| 7 - 12.9% | 35 ( 30.4) | 2 ( 22.2) | 185 ( 33.6) | |||
| Less than 7% | 24 ( 20.9) | 3 ( 33.3) | 159 ( 28.9) | |||
| NA | 2 ( 1.7) | 0 ( 0.0) | 2 ( 0.4) | |||
| U_R_F (%) | Metro | 97 ( 84.3) | 9 (100.0) | 451 ( 81.9) | 0.833 | |
| Urban | 14 ( 12.2) | 0 ( 0.0) | 74 ( 13.4) | |||
| Rural | 1 ( 0.9) | 0 ( 0.0) | 11 ( 2.0) | |||
| NA | 3 ( 2.6) | 0 ( 0.0) | 15 ( 2.7) | |||
| CROWFLY (mean (sd)) | 14.49 (19.23) | 28.98 (60.03) | 28.29 (124.92) | 0.499 | ||
| CDCC_TOTAL_BEST (%) | 0 | 90 ( 78.3) | 6 ( 66.7) | 462 ( 83.8) | 0.060 | |
| 1 | 13 ( 11.3) | 2 ( 22.2) | 65 ( 11.8) | |||
| 2 | 7 ( 6.1) | 0 ( 0.0) | 16 ( 2.9) | |||
| 3 | 5 ( 4.3) | 1 ( 11.1) | 8 ( 1.5) | |||
| SITE_TEXT (%) | C00.0 External Lip: Upper NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| C00.1 External Lip: Lower NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.2 External Lip: NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.3 Lip: Upper Mucosa | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.4 Lip: Lower Mucosa | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.5 Lip: Mucosa NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.6 Lip: Commissure | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.8 Lip: Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.9 Lip NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C01.9 Tongue: Base NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.0 Tongue: Dorsal NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.1 Tongue: Border, Tip | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.2 Tongue: Ventral NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.3 Tongue: Anterior NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.4 Lingual Tonsil | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.8 Tongue: Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.9 Tongue: NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.0 Gum: Upper | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.1 Gum: Lower | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.9 Gum NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.0 Mouth: Anterior Floor | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.1 Mouth: Lateral Floor | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.9 Floor of Mouth NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.0 Hard Palate | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.1 Soft Palate NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.2 Uvula | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.8 Palate: Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.9 Palate NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.0 Cheek Mucosa | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.1 Mouth: Vestibule | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.2 Retromolar Area | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.8 Mouth: Other Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.9 Mouth NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C07.9 Parotid Gland | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C09.8 Tonsil: Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C09.9 Tonsil NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C11.1 Nasopharynx: Poster Wall | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C14.2 Waldeyer Ring | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C30.0 Nasal Cavity | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C37.9 Thymus | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.0 Blood | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.2 Spleen | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.4 Hematopoietic NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.0 Skin of lip, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.1 Eyelid | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.2 External ear | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.3 Skin of ear and unspecified parts of face | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.4 Skin of scalp and neck | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.5 Skin of trunk | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.6 Skin of upper limb and shoulder | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.7 Skin of lower limb and hip | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.8 Overlapping lesion of skin | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.9 Skin, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C50.0 Nipple | 49 ( 42.6) | 6 ( 66.7) | 393 ( 71.3) | |||
| C51.0 Labium majus | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.1 Labium minus | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.2 Clitoris | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.8 Overlapping lesion of vulva | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.9 Vulva, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C52.9 Vagina, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.0 Prepuce | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.1 Glans penis | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.2 Body of penis | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.8 Overlapping lesion of penis | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.9 Penis | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C63.2 Scrotum, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.0 Lymph Nodes: HeadFaceNeck | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.1 Intrathoracic Lymph Nodes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.2 Intra-abdominal LymphNodes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.3 Lymph Nodes of axilla or arm | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.4 Lymph Nodes: Leg | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.5 Pelvic Lymph Nodes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.8 Lymph Nodes: multiple region | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.9 Lymph Node NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 66 ( 57.4) | 3 ( 33.3) | 158 ( 28.7) | |||
| BEHAVIOR (%) | 2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| 3 | 115 (100.0) | 9 (100.0) | 551 (100.0) | |||
| GRADE_F (%) | Gr I: Well Diff | 8 ( 7.0) | 0 ( 0.0) | 14 ( 2.5) | NaN | |
| Gr II: Mod Diff | 7 ( 6.1) | 0 ( 0.0) | 48 ( 8.7) | |||
| Gr III: Poor Diff | 17 ( 14.8) | 1 ( 11.1) | 88 ( 16.0) | |||
| Gr IV: Undiff/Anaplastic | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| 5 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 6 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 7 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 8 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA/Unkown | 83 ( 72.2) | 8 ( 88.9) | 399 ( 72.4) | |||
| DX_STAGING_PROC_DAYS (mean (sd)) | 3.10 (13.51) | 0.43 (1.13) | 1.36 (9.37) | 0.322 | ||
| TNM_CLIN_T (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 2 ( 1.7) | 0 ( 0.0) | 3 ( 0.5) | |||
| c1 | 7 ( 6.1) | 0 ( 0.0) | 30 ( 5.4) | |||
| c1A | 1 ( 0.9) | 0 ( 0.0) | 11 ( 2.0) | |||
| c1B | 4 ( 3.5) | 1 ( 11.1) | 4 ( 0.7) | |||
| c1C | 5 ( 4.3) | 0 ( 0.0) | 10 ( 1.8) | |||
| c1MI | 0 ( 0.0) | 0 ( 0.0) | 4 ( 0.7) | |||
| c2 | 7 ( 6.1) | 0 ( 0.0) | 14 ( 2.5) | |||
| c2A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2D | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3 | 5 ( 4.3) | 0 ( 0.0) | 13 ( 2.4) | |||
| c3A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c4 | 9 ( 7.8) | 0 ( 0.0) | 1 ( 0.2) | |||
| c4A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c4B | 4 ( 3.5) | 1 ( 11.1) | 5 ( 0.9) | |||
| c4C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c4D | 2 ( 1.7) | 0 ( 0.0) | 1 ( 0.2) | |||
| cX | 33 ( 28.7) | 1 ( 11.1) | 154 ( 27.9) | |||
| pA | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| pIS | 26 ( 22.6) | 5 ( 55.6) | 276 ( 50.1) | |||
| NA | 10 ( 8.7) | 1 ( 11.1) | 25 ( 4.5) | |||
| TNM_CLIN_N (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 55 ( 47.8) | 6 ( 66.7) | 388 ( 70.4) | |||
| c1 | 12 ( 10.4) | 0 ( 0.0) | 14 ( 2.5) | |||
| c1A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c1B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2 | 3 ( 2.6) | 1 ( 11.1) | 2 ( 0.4) | |||
| c2A | 1 ( 0.9) | 0 ( 0.0) | 1 ( 0.2) | |||
| c2B | 1 ( 0.9) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3 | 3 ( 2.6) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3A | 0 ( 0.0) | 0 ( 0.0) | 1 ( 0.2) | |||
| c3B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c4 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| cX | 31 ( 27.0) | 1 ( 11.1) | 130 ( 23.6) | |||
| NA | 9 ( 7.8) | 1 ( 11.1) | 15 ( 2.7) | |||
| TNM_CLIN_M (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 78 ( 67.8) | 8 ( 88.9) | 528 ( 95.8) | |||
| c0I+ | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c1 | 23 ( 20.0) | 0 ( 0.0) | 3 ( 0.5) | |||
| c1A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c1B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 14 ( 12.2) | 1 ( 11.1) | 20 ( 3.6) | |||
| TNM_CLIN_STAGE_GROUP (%) | 0 | 31 ( 27.0) | 4 ( 44.4) | 298 ( 54.1) | NaN | |
| 1 | 8 ( 7.0) | 1 ( 11.1) | 30 ( 5.4) | |||
| 1A | 4 ( 3.5) | 0 ( 0.0) | 28 ( 5.1) | |||
| 1B | 0 ( 0.0) | 0 ( 0.0) | 1 ( 0.2) | |||
| 1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 2A | 4 ( 3.5) | 0 ( 0.0) | 19 ( 3.4) | |||
| 2B | 5 ( 4.3) | 0 ( 0.0) | 8 ( 1.5) | |||
| 2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 3 | 1 ( 0.9) | 0 ( 0.0) | 1 ( 0.2) | |||
| 3A | 2 ( 1.7) | 1 ( 11.1) | 4 ( 0.7) | |||
| 3B | 6 ( 5.2) | 0 ( 0.0) | 6 ( 1.1) | |||
| 3C | 1 ( 0.9) | 0 ( 0.0) | 2 ( 0.4) | |||
| 4 | 24 ( 20.9) | 0 ( 0.0) | 3 ( 0.5) | |||
| 4A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A1 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 99 | 29 ( 25.2) | 3 ( 33.3) | 151 ( 27.4) | |||
| TNM_PATH_T (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 1 ( 0.9) | 0 ( 0.0) | 7 ( 1.3) | |||
| p1 | 0 ( 0.0) | 0 ( 0.0) | 13 ( 2.4) | |||
| p1A | 0 ( 0.0) | 0 ( 0.0) | 17 ( 3.1) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | 11 ( 2.0) | |||
| p1C | 1 ( 0.9) | 0 ( 0.0) | 23 ( 4.2) | |||
| p1MI | 0 ( 0.0) | 0 ( 0.0) | 7 ( 1.3) | |||
| p2 | 0 ( 0.0) | 0 ( 0.0) | 12 ( 2.2) | |||
| p2A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2D | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p3 | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| p3A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p3B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4 | 1 ( 0.9) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4B | 1 ( 0.9) | 0 ( 0.0) | 4 ( 0.7) | |||
| p4C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4D | 1 ( 0.9) | 0 ( 0.0) | 2 ( 0.4) | |||
| pA | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| pIS | 5 ( 4.3) | 0 ( 0.0) | 256 ( 46.5) | |||
| pX | 69 ( 60.0) | 4 ( 44.4) | 180 ( 32.7) | |||
| NA | 36 ( 31.3) | 5 ( 55.6) | 17 ( 3.1) | |||
| TNM_PATH_N (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 2 ( 1.7) | 0 ( 0.0) | 231 ( 41.9) | |||
| p0I- | 0 ( 0.0) | 0 ( 0.0) | 30 ( 5.4) | |||
| p0I+ | 1 ( 0.9) | 0 ( 0.0) | 1 ( 0.2) | |||
| p0M- | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p0M+ | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1 | 2 ( 1.7) | 0 ( 0.0) | 5 ( 0.9) | |||
| p1A | 1 ( 0.9) | 0 ( 0.0) | 8 ( 1.5) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1MI | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| p2 | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| p2A | 1 ( 0.9) | 0 ( 0.0) | 1 ( 0.2) | |||
| p2B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p3 | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| p3A | 0 ( 0.0) | 0 ( 0.0) | 3 ( 0.5) | |||
| p3B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p3C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| pX | 71 ( 61.7) | 4 ( 44.4) | 229 ( 41.6) | |||
| NA | 37 ( 32.2) | 5 ( 55.6) | 37 ( 6.7) | |||
| TNM_PATH_M (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1 | 5 ( 4.3) | 0 ( 0.0) | 3 ( 0.5) | |||
| p1A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| pX | 49 ( 42.6) | 3 ( 33.3) | 292 ( 53.0) | |||
| NA | 61 ( 53.0) | 6 ( 66.7) | 256 ( 46.5) | |||
| TNM_PATH_STAGE_GROUP (%) | 0 | 2 ( 1.7) | 0 ( 0.0) | 296 ( 53.7) | NaN | |
| 1 | 0 ( 0.0) | 0 ( 0.0) | 55 ( 10.0) | |||
| 1A | 0 ( 0.0) | 0 ( 0.0) | 26 ( 4.7) | |||
| 1B | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| 1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 2 | 0 ( 0.0) | 0 ( 0.0) | 3 ( 0.5) | |||
| 2A | 1 ( 0.9) | 0 ( 0.0) | 20 ( 3.6) | |||
| 2B | 0 ( 0.0) | 0 ( 0.0) | 8 ( 1.5) | |||
| 2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 3 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 3A | 1 ( 0.9) | 0 ( 0.0) | 3 ( 0.5) | |||
| 3B | 0 ( 0.0) | 0 ( 0.0) | 6 ( 1.1) | |||
| 3C | 1 ( 0.9) | 0 ( 0.0) | 4 ( 0.7) | |||
| 4 | 5 ( 4.3) | 0 ( 0.0) | 4 ( 0.7) | |||
| 4A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A1 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 99 | 81 ( 70.4) | 7 ( 77.8) | 110 ( 20.0) | |||
| NA | 24 ( 20.9) | 2 ( 22.2) | 14 ( 2.5) | |||
| DX_RX_STARTED_DAYS (mean (sd)) | 56.72 (74.66) | 60.00 (NA) | 33.70 (29.01) | NA | ||
| DX_SURG_STARTED_DAYS (mean (sd)) | NaN (NA) | NaN (NA) | 38.84 (40.14) | NA | ||
| DX_DEFSURG_STARTED_DAYS (mean (sd)) | NaN (NA) | NaN (NA) | 46.51 (46.12) | NA | ||
| MARGINS (%) | No Residual | 0 ( 0.0) | 0 ( 0.0) | 511 ( 92.7) | NaN | |
| Residual, NOS | 0 ( 0.0) | 0 ( 0.0) | 10 ( 1.8) | |||
| Microscopic Resid | 0 ( 0.0) | 0 ( 0.0) | 11 ( 2.0) | |||
| Macroscopic Resid | 0 ( 0.0) | 0 ( 0.0) | 3 ( 0.5) | |||
| Not evaluable | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| No surg | 115 (100.0) | 7 ( 77.8) | 0 ( 0.0) | |||
| Unknown | 0 ( 0.0) | 2 ( 22.2) | 16 ( 2.9) | |||
| MARGINS_YN (%) | No | 0 ( 0.0) | 0 ( 0.0) | 511 ( 92.7) | <0.001 | |
| Yes | 0 ( 0.0) | 0 ( 0.0) | 24 ( 4.4) | |||
| No surg/Unk/NA | 115 (100.0) | 9 (100.0) | 16 ( 2.9) | |||
| SURG_DISCHARGE_DAYS (mean (sd)) | NaN (NA) | NaN (NA) | 1.00 (4.05) | NA | ||
| READM_HOSP_30_DAYS_F (%) | No_Surg_or_No_Readmit | 109 ( 94.8) | 9 (100.0) | 509 ( 92.4) | 0.156 | |
| Unplan_Readmit_Same | 1 ( 0.9) | 0 ( 0.0) | 14 ( 2.5) | |||
| Plan_Readmit_Same | 0 ( 0.0) | 0 ( 0.0) | 18 ( 3.3) | |||
| PlanUnplan_Same | 1 ( 0.9) | 0 ( 0.0) | 0 ( 0.0) | |||
| 9 | 4 ( 3.5) | 0 ( 0.0) | 10 ( 1.8) | |||
| RX_SUMM_RADIATION_F (%) | None | 94 ( 81.7) | 8 ( 88.9) | 363 ( 65.9) | NaN | |
| Beam Radiation | 19 ( 16.5) | 0 ( 0.0) | 181 ( 32.8) | |||
| Radioactive Implants | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| Radioisotopes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Beam + Imp or Isotopes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Radiation, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 2 ( 1.7) | 1 ( 11.1) | 5 ( 0.9) | |||
| PUF_30_DAY_MORT_CD_F (%) | Alive_30 | 0 ( 0.0) | 0 ( 0.0) | 542 ( 98.4) | <0.001 | |
| Dead_30 | 0 ( 0.0) | 0 ( 0.0) | 1 ( 0.2) | |||
| Unknown | 0 ( 0.0) | 0 ( 0.0) | 6 ( 1.1) | |||
| NA | 115 (100.0) | 9 (100.0) | 2 ( 0.4) | |||
| PUF_90_DAY_MORT_CD_F (%) | Alive_90 | 0 ( 0.0) | 0 ( 0.0) | 536 ( 97.3) | <0.001 | |
| Dead_90 | 0 ( 0.0) | 0 ( 0.0) | 3 ( 0.5) | |||
| Unknown | 0 ( 0.0) | 0 ( 0.0) | 10 ( 1.8) | |||
| NA | 115 (100.0) | 9 (100.0) | 2 ( 0.4) | |||
| DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) | 29.20 (33.73) | 11.00 (11.63) | 66.23 (41.60) | <0.001 | ||
| LYMPH_VASCULAR_INVASION_F (%) | Neg_LymphVasc_Inv | 12 ( 10.4) | 3 ( 33.3) | 147 ( 26.7) | NaN | |
| Pos_LumphVasc_Inv | 0 ( 0.0) | 0 ( 0.0) | 10 ( 1.8) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 51 ( 44.3) | 3 ( 33.3) | 100 ( 18.1) | |||
| NA | 52 ( 45.2) | 3 ( 33.3) | 294 ( 53.4) | |||
| RX_HOSP_SURG_APPR_2010_F (%) | No_Surg | 63 ( 54.8) | 6 ( 66.7) | 27 ( 4.9) | NaN | |
| Robot_Assist | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Robot_to_Open | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Endo_Lap | 0 ( 0.0) | 0 ( 0.0) | 1 ( 0.2) | |||
| Endo_Lap_to_Open | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Open_Unknown | 0 ( 0.0) | 0 ( 0.0) | 229 ( 41.6) | |||
| Unknown | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 52 ( 45.2) | 3 ( 33.3) | 294 ( 53.4) | |||
| SURG_RAD_SEQ (%) | Surg Alone | 0 ( 0.0) | 0 ( 0.0) | 363 ( 65.9) | NaN | |
| Surg then Rad | 0 ( 0.0) | 0 ( 0.0) | 181 ( 32.8) | |||
| Rad Alone | 19 ( 16.5) | 0 ( 0.0) | 0 ( 0.0) | |||
| No Treatment | 95 ( 82.6) | 0 ( 0.0) | 0 ( 0.0) | |||
| Other | 1 ( 0.9) | 9 (100.0) | 5 ( 0.9) | |||
| Rad before and after Surg | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad then Surg | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| SURG_RAD_SEQ_C (%) | Surg, No rad, No Chemo | 0 ( 0.0) | 0 ( 0.0) | 321 ( 58.3) | NaN | |
| Surg then Rad, No Chemo | 0 ( 0.0) | 0 ( 0.0) | 141 ( 25.6) | |||
| Surg then Rad, Yes Chemo | 0 ( 0.0) | 0 ( 0.0) | 33 ( 6.0) | |||
| Surg, No rad, Yes Chemo | 0 ( 0.0) | 0 ( 0.0) | 25 ( 4.5) | |||
| No Surg, No Rad, Yes Chemo | 9 ( 7.8) | 0 ( 0.0) | 0 ( 0.0) | |||
| No Surg, No Rad, No Chemo | 81 ( 70.4) | 0 ( 0.0) | 0 ( 0.0) | |||
| Other | 8 ( 7.0) | 9 (100.0) | 29 ( 5.3) | |||
| Rad, No Surg, Yes Chemo | 5 ( 4.3) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad, No Surg, No Chemo | 12 ( 10.4) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad then Surg, Yes Chemo | 0 ( 0.0) | 0 ( 0.0) | 2 ( 0.4) | |||
| Rad then Surg, No Chemo | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad before and after Surg, Yes Chemo | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad before and after Surg, No Chemo | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| T_SIZE (%) | No Tumor | 4 ( 3.5) | 0 ( 0.0) | 0 ( 0.0) | <0.001 | |
| Microscopic focus | 1 ( 0.9) | 0 ( 0.0) | 16 ( 2.9) | |||
| < 1 cm | 12 ( 10.4) | 2 ( 22.2) | 70 ( 12.7) | |||
| 1-2 cm | 8 ( 7.0) | 0 ( 0.0) | 83 ( 15.1) | |||
| 2-3 cm | 7 ( 6.1) | 1 ( 11.1) | 45 ( 8.2) | |||
| 3-4 cm | 6 ( 5.2) | 0 ( 0.0) | 21 ( 3.8) | |||
| 4-5 cm | 5 ( 4.3) | 0 ( 0.0) | 6 ( 1.1) | |||
| 5-6 cm | 3 ( 2.6) | 0 ( 0.0) | 5 ( 0.9) | |||
| >6 cm | 11 ( 9.6) | 0 ( 0.0) | 20 ( 3.6) | |||
| NA_unk | 58 ( 50.4) | 6 ( 66.7) | 285 ( 51.7) | |||
| SURGERY_YN (%) | No | 115 (100.0) | 0 ( 0.0) | 0 ( 0.0) | <0.001 | |
| Ukn | 0 ( 0.0) | 9 (100.0) | 0 ( 0.0) | |||
| Yes | 0 ( 0.0) | 0 ( 0.0) | 551 (100.0) | |||
| RADIATION_YN (%) | No | 95 ( 82.6) | 8 ( 88.9) | 363 ( 65.9) | <0.001 | |
| Yes | 19 ( 16.5) | 0 ( 0.0) | 183 ( 33.2) | |||
| NA | 1 ( 0.9) | 1 ( 11.1) | 5 ( 0.9) | |||
| CHEMO_YN (%) | No | 93 ( 80.9) | 7 ( 77.8) | 463 ( 84.0) | 0.175 | |
| Yes | 14 ( 12.2) | 0 ( 0.0) | 60 ( 10.9) | |||
| Ukn | 8 ( 7.0) | 2 ( 22.2) | 28 ( 5.1) | |||
| IMMUNO_YN (%) | No | 112 ( 97.4) | 8 ( 88.9) | 537 ( 97.5) | 0.226 | |
| Yes | 0 ( 0.0) | 0 ( 0.0) | 5 ( 0.9) | |||
| Ukn | 3 ( 2.6) | 1 ( 11.1) | 9 ( 1.6) | |||
| Tx_YN (%) | FALSE | 80 ( 69.6) | 0 ( 0.0) | 0 ( 0.0) | <0.001 | |
| TRUE | 27 ( 23.5) | 7 ( 77.8) | 523 ( 94.9) | |||
| NA | 8 ( 7.0) | 2 ( 22.2) | 28 ( 5.1) | |||
| mets_at_dx (%) | Bone | 9 ( 7.8) | 0 ( 0.0) | 1 ( 0.2) | NaN | |
| Brain | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Liver | 0 ( 0.0) | 0 ( 0.0) | 1 ( 0.2) | |||
| Lung | 4 ( 3.5) | 0 ( 0.0) | 0 ( 0.0) | |||
| None/Other/Unk/NA | 102 ( 88.7) | 9 (100.0) | 549 ( 99.6) | |||
| MEDICAID_EXPN_CODE (%) | Non-Expansion State | 48 ( 41.7) | 2 ( 22.2) | 211 ( 38.3) | 0.588 | |
| Jan 2014 Expansion States | 38 ( 33.0) | 3 ( 33.3) | 166 ( 30.1) | |||
| Early Expansion States (2010-13) | 12 ( 10.4) | 2 ( 22.2) | 63 ( 11.4) | |||
| Late Expansion States (> Jan 2014) | 15 ( 13.0) | 2 ( 22.2) | 78 ( 14.2) | |||
| Suppressed for Ages 0 - 39 | 2 ( 1.7) | 0 ( 0.0) | 33 ( 6.0) |
p_table(data,
vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY", "AGE", "AGE_F", "AGE_40",
"SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F",
"INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
"SITE_TEXT", "BEHAVIOR", "GRADE_F",
"DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
"TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
"TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
"DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
"READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
"PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS",
"LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
"SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN",
"CHEMO_YN", "IMMUNO_YN", "Tx_YN", "mets_at_dx",
"MEDICAID_EXPN_CODE"),
strata = "RADIATION_YN")
| level | No | Yes | p | test | |
|---|---|---|---|---|---|
| n | 466 | 202 | |||
| FACILITY_TYPE_F (%) | Community Cancer Program | 63 ( 13.5) | 17 ( 8.4) | 0.217 | |
| Comprehensive Comm Ca Program | 202 ( 43.3) | 89 ( 44.1) | |||
| Academic/Research Program | 112 ( 24.0) | 61 ( 30.2) | |||
| Integrated Network Ca Program | 66 ( 14.2) | 24 ( 11.9) | |||
| NA | 23 ( 4.9) | 11 ( 5.4) | |||
| FACILITY_LOCATION_F (%) | New England | 18 ( 3.9) | 12 ( 5.9) | 0.148 | |
| Middle Atlantic | 65 ( 13.9) | 27 ( 13.4) | |||
| South Atlantic | 102 ( 21.9) | 41 ( 20.3) | |||
| East North Central | 101 ( 21.7) | 33 ( 16.3) | |||
| East South Central | 31 ( 6.7) | 13 ( 6.4) | |||
| West North Central | 42 ( 9.0) | 17 ( 8.4) | |||
| West South Central | 39 ( 8.4) | 19 ( 9.4) | |||
| Mountain | 15 ( 3.2) | 18 ( 8.9) | |||
| Pacific | 30 ( 6.4) | 11 ( 5.4) | |||
| NA | 23 ( 4.9) | 11 ( 5.4) | |||
| FACILITY_GEOGRAPHY (%) | Northeast | 83 ( 17.8) | 39 ( 19.3) | 0.334 | |
| South | 141 ( 30.3) | 60 ( 29.7) | |||
| Midwest | 174 ( 37.3) | 63 ( 31.2) | |||
| West | 45 ( 9.7) | 29 ( 14.4) | |||
| NA | 23 ( 4.9) | 11 ( 5.4) | |||
| AGE (mean (sd)) | 67.48 (15.44) | 60.96 (12.94) | <0.001 | ||
| AGE_F (%) | (0,54] | 98 ( 21.0) | 62 ( 30.7) | <0.001 | |
| (54,64] | 94 ( 20.2) | 54 ( 26.7) | |||
| (64,74] | 91 ( 19.5) | 56 ( 27.7) | |||
| (74,100] | 183 ( 39.3) | 30 ( 14.9) | |||
| AGE_40 (%) | (0,40] | 23 ( 4.9) | 14 ( 6.9) | 0.395 | |
| (40,100] | 443 ( 95.1) | 188 ( 93.1) | |||
| SEX_F (%) | Male | 14 ( 3.0) | 5 ( 2.5) | 0.901 | |
| Female | 452 ( 97.0) | 197 ( 97.5) | |||
| RACE_F (%) | White | 398 ( 85.4) | 167 ( 82.7) | 0.846 | |
| Black | 50 ( 10.7) | 26 ( 12.9) | |||
| Other/Unk | 10 ( 2.1) | 5 ( 2.5) | |||
| Asian | 8 ( 1.7) | 4 ( 2.0) | |||
| HISPANIC (%) | No | 402 ( 86.3) | 179 ( 88.6) | 0.708 | |
| Yes | 20 ( 4.3) | 7 ( 3.5) | |||
| Unknown | 44 ( 9.4) | 16 ( 7.9) | |||
| INSURANCE_F (%) | Private | 168 ( 36.1) | 103 ( 51.0) | 0.002 | |
| None | 16 ( 3.4) | 8 ( 4.0) | |||
| Medicaid | 25 ( 5.4) | 10 ( 5.0) | |||
| Medicare | 239 ( 51.3) | 76 ( 37.6) | |||
| Other Government | 4 ( 0.9) | 4 ( 2.0) | |||
| Unknown | 14 ( 3.0) | 1 ( 0.5) | |||
| INCOME_F (%) | Less than $38,000 | 94 ( 20.2) | 35 ( 17.3) | 0.166 | |
| $38,000 - $47,999 | 104 ( 22.3) | 38 ( 18.8) | |||
| $48,000 - $62,999 | 128 ( 27.5) | 48 ( 23.8) | |||
| $63,000 + | 137 ( 29.4) | 79 ( 39.1) | |||
| NA | 3 ( 0.6) | 2 ( 1.0) | |||
| EDUCATION_F (%) | 21% or more | 72 ( 15.5) | 25 ( 12.4) | 0.406 | |
| 13 - 20.9% | 119 ( 25.5) | 45 ( 22.3) | |||
| 7 - 12.9% | 154 ( 33.0) | 66 ( 32.7) | |||
| Less than 7% | 118 ( 25.3) | 65 ( 32.2) | |||
| NA | 3 ( 0.6) | 1 ( 0.5) | |||
| U_R_F (%) | Metro | 370 ( 79.4) | 180 ( 89.1) | 0.006 | |
| Urban | 74 ( 15.9) | 14 ( 6.9) | |||
| Rural | 7 ( 1.5) | 5 ( 2.5) | |||
| NA | 15 ( 3.2) | 3 ( 1.5) | |||
| CROWFLY (mean (sd)) | 31.23 (135.66) | 14.58 (21.82) | 0.084 | ||
| CDCC_TOTAL_BEST (%) | 0 | 376 ( 80.7) | 175 ( 86.6) | 0.204 | |
| 1 | 64 ( 13.7) | 16 ( 7.9) | |||
| 2 | 16 ( 3.4) | 7 ( 3.5) | |||
| 3 | 10 ( 2.1) | 4 ( 2.0) | |||
| SITE_TEXT (%) | C00.0 External Lip: Upper NOS | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| C00.1 External Lip: Lower NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.2 External Lip: NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.3 Lip: Upper Mucosa | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.4 Lip: Lower Mucosa | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.5 Lip: Mucosa NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.6 Lip: Commissure | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.8 Lip: Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.9 Lip NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C01.9 Tongue: Base NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.0 Tongue: Dorsal NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.1 Tongue: Border, Tip | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.2 Tongue: Ventral NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.3 Tongue: Anterior NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.4 Lingual Tonsil | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.8 Tongue: Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.9 Tongue: NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.0 Gum: Upper | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.1 Gum: Lower | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.9 Gum NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.0 Mouth: Anterior Floor | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.1 Mouth: Lateral Floor | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.9 Floor of Mouth NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.0 Hard Palate | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.1 Soft Palate NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.2 Uvula | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.8 Palate: Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.9 Palate NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.0 Cheek Mucosa | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.1 Mouth: Vestibule | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.2 Retromolar Area | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.8 Mouth: Other Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.9 Mouth NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C07.9 Parotid Gland | 0 ( 0.0) | 0 ( 0.0) | |||
| C09.8 Tonsil: Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C09.9 Tonsil NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C11.1 Nasopharynx: Poster Wall | 0 ( 0.0) | 0 ( 0.0) | |||
| C14.2 Waldeyer Ring | 0 ( 0.0) | 0 ( 0.0) | |||
| C30.0 Nasal Cavity | 0 ( 0.0) | 0 ( 0.0) | |||
| C37.9 Thymus | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.0 Blood | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.2 Spleen | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.4 Hematopoietic NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.0 Skin of lip, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.1 Eyelid | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.2 External ear | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.3 Skin of ear and unspecified parts of face | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.4 Skin of scalp and neck | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.5 Skin of trunk | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.6 Skin of upper limb and shoulder | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.7 Skin of lower limb and hip | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.8 Overlapping lesion of skin | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.9 Skin, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C50.0 Nipple | 316 ( 67.8) | 126 ( 62.4) | |||
| C51.0 Labium majus | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.1 Labium minus | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.2 Clitoris | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.8 Overlapping lesion of vulva | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.9 Vulva, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C52.9 Vagina, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.0 Prepuce | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.1 Glans penis | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.2 Body of penis | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.8 Overlapping lesion of penis | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.9 Penis | 0 ( 0.0) | 0 ( 0.0) | |||
| C63.2 Scrotum, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.0 Lymph Nodes: HeadFaceNeck | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.1 Intrathoracic Lymph Nodes | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.2 Intra-abdominal LymphNodes | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.3 Lymph Nodes of axilla or arm | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.4 Lymph Nodes: Leg | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.5 Pelvic Lymph Nodes | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.8 Lymph Nodes: multiple region | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.9 Lymph Node NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 150 ( 32.2) | 76 ( 37.6) | |||
| BEHAVIOR (%) | 2 | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| 3 | 466 (100.0) | 202 (100.0) | |||
| GRADE_F (%) | Gr I: Well Diff | 14 ( 3.0) | 8 ( 4.0) | NaN | |
| Gr II: Mod Diff | 36 ( 7.7) | 19 ( 9.4) | |||
| Gr III: Poor Diff | 66 ( 14.2) | 38 ( 18.8) | |||
| Gr IV: Undiff/Anaplastic | 2 ( 0.4) | 0 ( 0.0) | |||
| 5 | 0 ( 0.0) | 0 ( 0.0) | |||
| 6 | 0 ( 0.0) | 0 ( 0.0) | |||
| 7 | 0 ( 0.0) | 0 ( 0.0) | |||
| 8 | 0 ( 0.0) | 0 ( 0.0) | |||
| NA/Unkown | 348 ( 74.7) | 137 ( 67.8) | |||
| DX_STAGING_PROC_DAYS (mean (sd)) | 1.54 (10.16) | 1.99 (10.52) | 0.654 | ||
| TNM_CLIN_T (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 3 ( 0.6) | 2 ( 1.0) | |||
| c1 | 25 ( 5.4) | 12 ( 5.9) | |||
| c1A | 6 ( 1.3) | 6 ( 3.0) | |||
| c1B | 7 ( 1.5) | 2 ( 1.0) | |||
| c1C | 8 ( 1.7) | 7 ( 3.5) | |||
| c1MI | 3 ( 0.6) | 1 ( 0.5) | |||
| c2 | 14 ( 3.0) | 7 ( 3.5) | |||
| c2A | 0 ( 0.0) | 0 ( 0.0) | |||
| c2B | 0 ( 0.0) | 0 ( 0.0) | |||
| c2C | 0 ( 0.0) | 0 ( 0.0) | |||
| c2D | 0 ( 0.0) | 0 ( 0.0) | |||
| c3 | 11 ( 2.4) | 7 ( 3.5) | |||
| c3A | 0 ( 0.0) | 0 ( 0.0) | |||
| c3B | 0 ( 0.0) | 0 ( 0.0) | |||
| c4 | 7 ( 1.5) | 3 ( 1.5) | |||
| c4A | 0 ( 0.0) | 0 ( 0.0) | |||
| c4B | 7 ( 1.5) | 3 ( 1.5) | |||
| c4C | 0 ( 0.0) | 0 ( 0.0) | |||
| c4D | 3 ( 0.6) | 0 ( 0.0) | |||
| cX | 136 ( 29.2) | 51 ( 25.2) | |||
| pA | 0 ( 0.0) | 0 ( 0.0) | |||
| pIS | 210 ( 45.1) | 92 ( 45.5) | |||
| NA | 26 ( 5.6) | 9 ( 4.5) | |||
| TNM_CLIN_N (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 312 ( 67.0) | 132 ( 65.3) | |||
| c1 | 12 ( 2.6) | 14 ( 6.9) | |||
| c1A | 0 ( 0.0) | 0 ( 0.0) | |||
| c1B | 0 ( 0.0) | 0 ( 0.0) | |||
| c2 | 3 ( 0.6) | 3 ( 1.5) | |||
| c2A | 0 ( 0.0) | 2 ( 1.0) | |||
| c2B | 1 ( 0.2) | 0 ( 0.0) | |||
| c2C | 0 ( 0.0) | 0 ( 0.0) | |||
| c3 | 3 ( 0.6) | 0 ( 0.0) | |||
| c3A | 0 ( 0.0) | 1 ( 0.5) | |||
| c3B | 0 ( 0.0) | 0 ( 0.0) | |||
| c3C | 0 ( 0.0) | 0 ( 0.0) | |||
| c4 | 0 ( 0.0) | 0 ( 0.0) | |||
| cX | 116 ( 24.9) | 44 ( 21.8) | |||
| NA | 19 ( 4.1) | 6 ( 3.0) | |||
| TNM_CLIN_M (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 424 ( 91.0) | 184 ( 91.1) | |||
| c0I+ | 0 ( 0.0) | 0 ( 0.0) | |||
| c1 | 16 ( 3.4) | 10 ( 5.0) | |||
| c1A | 0 ( 0.0) | 0 ( 0.0) | |||
| c1B | 0 ( 0.0) | 0 ( 0.0) | |||
| c1C | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 26 ( 5.6) | 8 ( 4.0) | |||
| TNM_CLIN_STAGE_GROUP (%) | 0 | 235 ( 50.4) | 93 ( 46.0) | NaN | |
| 1 | 29 ( 6.2) | 10 ( 5.0) | |||
| 1A | 18 ( 3.9) | 14 ( 6.9) | |||
| 1B | 0 ( 0.0) | 1 ( 0.5) | |||
| 1C | 0 ( 0.0) | 0 ( 0.0) | |||
| 2 | 0 ( 0.0) | 0 ( 0.0) | |||
| 2A | 15 ( 3.2) | 8 ( 4.0) | |||
| 2B | 10 ( 2.1) | 3 ( 1.5) | |||
| 2C | 0 ( 0.0) | 0 ( 0.0) | |||
| 3 | 2 ( 0.4) | 0 ( 0.0) | |||
| 3A | 3 ( 0.6) | 4 ( 2.0) | |||
| 3B | 7 ( 1.5) | 5 ( 2.5) | |||
| 3C | 1 ( 0.2) | 2 ( 1.0) | |||
| 4 | 17 ( 3.6) | 10 ( 5.0) | |||
| 4A | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A1 | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A2 | 0 ( 0.0) | 0 ( 0.0) | |||
| 4B | 0 ( 0.0) | 0 ( 0.0) | |||
| 4C | 0 ( 0.0) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | |||
| 99 | 129 ( 27.7) | 52 ( 25.7) | |||
| TNM_PATH_T (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 4 ( 0.9) | 4 ( 2.0) | |||
| p1 | 5 ( 1.1) | 8 ( 4.0) | |||
| p1A | 10 ( 2.1) | 7 ( 3.5) | |||
| p1B | 4 ( 0.9) | 7 ( 3.5) | |||
| p1C | 12 ( 2.6) | 12 ( 5.9) | |||
| p1MI | 6 ( 1.3) | 1 ( 0.5) | |||
| p2 | 7 ( 1.5) | 5 ( 2.5) | |||
| p2A | 0 ( 0.0) | 0 ( 0.0) | |||
| p2B | 0 ( 0.0) | 0 ( 0.0) | |||
| p2C | 0 ( 0.0) | 0 ( 0.0) | |||
| p2D | 0 ( 0.0) | 0 ( 0.0) | |||
| p3 | 1 ( 0.2) | 1 ( 0.5) | |||
| p3A | 0 ( 0.0) | 0 ( 0.0) | |||
| p3B | 0 ( 0.0) | 0 ( 0.0) | |||
| p4 | 1 ( 0.2) | 0 ( 0.0) | |||
| p4A | 0 ( 0.0) | 0 ( 0.0) | |||
| p4B | 5 ( 1.1) | 0 ( 0.0) | |||
| p4C | 0 ( 0.0) | 0 ( 0.0) | |||
| p4D | 2 ( 0.4) | 1 ( 0.5) | |||
| pA | 0 ( 0.0) | 0 ( 0.0) | |||
| pIS | 174 ( 37.3) | 82 ( 40.6) | |||
| pX | 190 ( 40.8) | 62 ( 30.7) | |||
| NA | 45 ( 9.7) | 12 ( 5.9) | |||
| TNM_PATH_N (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 151 ( 32.4) | 79 ( 39.1) | |||
| p0I- | 18 ( 3.9) | 12 ( 5.9) | |||
| p0I+ | 2 ( 0.4) | 0 ( 0.0) | |||
| p0M- | 0 ( 0.0) | 0 ( 0.0) | |||
| p0M+ | 0 ( 0.0) | 0 ( 0.0) | |||
| p1 | 3 ( 0.6) | 4 ( 2.0) | |||
| p1A | 4 ( 0.9) | 5 ( 2.5) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | |||
| p1C | 0 ( 0.0) | 0 ( 0.0) | |||
| p1MI | 0 ( 0.0) | 2 ( 1.0) | |||
| p2 | 0 ( 0.0) | 2 ( 1.0) | |||
| p2A | 1 ( 0.2) | 1 ( 0.5) | |||
| p2B | 0 ( 0.0) | 0 ( 0.0) | |||
| p2C | 0 ( 0.0) | 0 ( 0.0) | |||
| p3 | 2 ( 0.4) | 0 ( 0.0) | |||
| p3A | 1 ( 0.2) | 2 ( 1.0) | |||
| p3B | 0 ( 0.0) | 0 ( 0.0) | |||
| p3C | 0 ( 0.0) | 0 ( 0.0) | |||
| p4 | 0 ( 0.0) | 0 ( 0.0) | |||
| pX | 222 ( 47.6) | 79 ( 39.1) | |||
| NA | 62 ( 13.3) | 16 ( 7.9) | |||
| TNM_PATH_M (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 0 ( 0.0) | 0 ( 0.0) | |||
| p1 | 6 ( 1.3) | 2 ( 1.0) | |||
| p1A | 0 ( 0.0) | 0 ( 0.0) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | |||
| p1C | 0 ( 0.0) | 0 ( 0.0) | |||
| pX | 238 ( 51.1) | 105 ( 52.0) | |||
| NA | 222 ( 47.6) | 95 ( 47.0) | |||
| TNM_PATH_STAGE_GROUP (%) | 0 | 208 ( 44.6) | 86 ( 42.6) | NaN | |
| 1 | 36 ( 7.7) | 19 ( 9.4) | |||
| 1A | 14 ( 3.0) | 12 ( 5.9) | |||
| 1B | 0 ( 0.0) | 2 ( 1.0) | |||
| 1C | 0 ( 0.0) | 0 ( 0.0) | |||
| 2 | 2 ( 0.4) | 1 ( 0.5) | |||
| 2A | 10 ( 2.1) | 11 ( 5.4) | |||
| 2B | 5 ( 1.1) | 3 ( 1.5) | |||
| 2C | 0 ( 0.0) | 0 ( 0.0) | |||
| 3 | 0 ( 0.0) | 0 ( 0.0) | |||
| 3A | 0 ( 0.0) | 4 ( 2.0) | |||
| 3B | 5 ( 1.1) | 1 ( 0.5) | |||
| 3C | 3 ( 0.6) | 2 ( 1.0) | |||
| 4 | 6 ( 1.3) | 3 ( 1.5) | |||
| 4A | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A1 | 0 ( 0.0) | 0 ( 0.0) | |||
| 4B | 0 ( 0.0) | 0 ( 0.0) | |||
| 4C | 0 ( 0.0) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | |||
| 99 | 145 ( 31.1) | 50 ( 24.8) | |||
| NA | 32 ( 6.9) | 8 ( 4.0) | |||
| DX_RX_STARTED_DAYS (mean (sd)) | 34.96 (36.22) | 36.30 (32.06) | 0.659 | ||
| DX_SURG_STARTED_DAYS (mean (sd)) | 35.59 (35.24) | 45.29 (47.87) | 0.008 | ||
| DX_DEFSURG_STARTED_DAYS (mean (sd)) | 43.22 (43.37) | 53.04 (50.87) | 0.020 | ||
| MARGINS (%) | No Residual | 341 ( 73.2) | 165 ( 81.7) | NaN | |
| Residual, NOS | 5 ( 1.1) | 5 ( 2.5) | |||
| Microscopic Resid | 4 ( 0.9) | 7 ( 3.5) | |||
| Macroscopic Resid | 2 ( 0.4) | 1 ( 0.5) | |||
| Not evaluable | 0 ( 0.0) | 0 ( 0.0) | |||
| No surg | 102 ( 21.9) | 19 ( 9.4) | |||
| Unknown | 12 ( 2.6) | 5 ( 2.5) | |||
| MARGINS_YN (%) | No | 341 ( 73.2) | 165 ( 81.7) | <0.001 | |
| Yes | 11 ( 2.4) | 13 ( 6.4) | |||
| No surg/Unk/NA | 114 ( 24.5) | 24 ( 11.9) | |||
| SURG_DISCHARGE_DAYS (mean (sd)) | 0.99 (1.60) | 1.06 (6.83) | 0.859 | ||
| READM_HOSP_30_DAYS_F (%) | No_Surg_or_No_Readmit | 434 ( 93.1) | 188 ( 93.1) | 0.343 | |
| Unplan_Readmit_Same | 12 ( 2.6) | 2 ( 1.0) | |||
| Plan_Readmit_Same | 12 ( 2.6) | 6 ( 3.0) | |||
| PlanUnplan_Same | 0 ( 0.0) | 1 ( 0.5) | |||
| 9 | 8 ( 1.7) | 5 ( 2.5) | |||
| RX_SUMM_RADIATION_F (%) | None | 465 ( 99.8) | 0 ( 0.0) | NaN | |
| Beam Radiation | 0 ( 0.0) | 200 ( 99.0) | |||
| Radioactive Implants | 0 ( 0.0) | 2 ( 1.0) | |||
| Radioisotopes | 0 ( 0.0) | 0 ( 0.0) | |||
| Beam + Imp or Isotopes | 0 ( 0.0) | 0 ( 0.0) | |||
| Radiation, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 1 ( 0.2) | 0 ( 0.0) | |||
| PUF_30_DAY_MORT_CD_F (%) | Alive_30 | 356 ( 76.4) | 181 ( 89.6) | 0.001 | |
| Dead_30 | 1 ( 0.2) | 0 ( 0.0) | |||
| Unknown | 4 ( 0.9) | 2 ( 1.0) | |||
| NA | 105 ( 22.5) | 19 ( 9.4) | |||
| PUF_90_DAY_MORT_CD_F (%) | Alive_90 | 351 ( 75.3) | 180 ( 89.1) | <0.001 | |
| Dead_90 | 3 ( 0.6) | 0 ( 0.0) | |||
| Unknown | 7 ( 1.5) | 3 ( 1.5) | |||
| NA | 105 ( 22.5) | 19 ( 9.4) | |||
| DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) | 56.30 (41.93) | 66.16 (43.99) | 0.006 | ||
| LYMPH_VASCULAR_INVASION_F (%) | Neg_LymphVasc_Inv | 110 ( 23.6) | 49 ( 24.3) | NaN | |
| Pos_LumphVasc_Inv | 4 ( 0.9) | 6 ( 3.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 111 ( 23.8) | 40 ( 19.8) | |||
| NA | 241 ( 51.7) | 107 ( 53.0) | |||
| RX_HOSP_SURG_APPR_2010_F (%) | No_Surg | 66 ( 14.2) | 28 ( 13.9) | NaN | |
| Robot_Assist | 0 ( 0.0) | 0 ( 0.0) | |||
| Robot_to_Open | 0 ( 0.0) | 0 ( 0.0) | |||
| Endo_Lap | 1 ( 0.2) | 0 ( 0.0) | |||
| Endo_Lap_to_Open | 0 ( 0.0) | 0 ( 0.0) | |||
| Open_Unknown | 158 ( 33.9) | 67 ( 33.2) | |||
| Unknown | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 241 ( 51.7) | 107 ( 53.0) | |||
| SURG_RAD_SEQ (%) | Surg Alone | 363 ( 77.9) | 0 ( 0.0) | NaN | |
| Surg then Rad | 0 ( 0.0) | 181 ( 89.6) | |||
| Rad Alone | 0 ( 0.0) | 19 ( 9.4) | |||
| No Treatment | 95 ( 20.4) | 0 ( 0.0) | |||
| Other | 8 ( 1.7) | 0 ( 0.0) | |||
| Rad before and after Surg | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad then Surg | 0 ( 0.0) | 2 ( 1.0) | |||
| SURG_RAD_SEQ_C (%) | Surg, No rad, No Chemo | 321 ( 68.9) | 0 ( 0.0) | NaN | |
| Surg then Rad, No Chemo | 0 ( 0.0) | 141 ( 69.8) | |||
| Surg then Rad, Yes Chemo | 0 ( 0.0) | 33 ( 16.3) | |||
| Surg, No rad, Yes Chemo | 25 ( 5.4) | 0 ( 0.0) | |||
| No Surg, No Rad, Yes Chemo | 9 ( 1.9) | 0 ( 0.0) | |||
| No Surg, No Rad, No Chemo | 81 ( 17.4) | 0 ( 0.0) | |||
| Other | 30 ( 6.4) | 9 ( 4.5) | |||
| Rad, No Surg, Yes Chemo | 0 ( 0.0) | 5 ( 2.5) | |||
| Rad, No Surg, No Chemo | 0 ( 0.0) | 12 ( 5.9) | |||
| Rad then Surg, Yes Chemo | 0 ( 0.0) | 2 ( 1.0) | |||
| Rad then Surg, No Chemo | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad before and after Surg, Yes Chemo | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad before and after Surg, No Chemo | 0 ( 0.0) | 0 ( 0.0) | |||
| T_SIZE (%) | No Tumor | 2 ( 0.4) | 2 ( 1.0) | 0.012 | |
| Microscopic focus | 8 ( 1.7) | 9 ( 4.5) | |||
| < 1 cm | 57 ( 12.2) | 25 ( 12.4) | |||
| 1-2 cm | 49 ( 10.5) | 41 ( 20.3) | |||
| 2-3 cm | 37 ( 7.9) | 16 ( 7.9) | |||
| 3-4 cm | 20 ( 4.3) | 7 ( 3.5) | |||
| 4-5 cm | 9 ( 1.9) | 2 ( 1.0) | |||
| 5-6 cm | 5 ( 1.1) | 3 ( 1.5) | |||
| >6 cm | 20 ( 4.3) | 11 ( 5.4) | |||
| NA_unk | 259 ( 55.6) | 86 ( 42.6) | |||
| SURGERY_YN (%) | No | 95 ( 20.4) | 19 ( 9.4) | <0.001 | |
| Ukn | 8 ( 1.7) | 0 ( 0.0) | |||
| Yes | 363 ( 77.9) | 183 ( 90.6) | |||
| RADIATION_YN (%) | No | 466 (100.0) | 0 ( 0.0) | NaN | |
| Yes | 0 ( 0.0) | 202 (100.0) | |||
| NA | 0 ( 0.0) | 0 ( 0.0) | |||
| CHEMO_YN (%) | No | 409 ( 87.8) | 153 ( 75.7) | <0.001 | |
| Yes | 34 ( 7.3) | 40 ( 19.8) | |||
| Ukn | 23 ( 4.9) | 9 ( 4.5) | |||
| IMMUNO_YN (%) | No | 457 ( 98.1) | 197 ( 97.5) | 0.873 | |
| Yes | 3 ( 0.6) | 2 ( 1.0) | |||
| Ukn | 6 ( 1.3) | 3 ( 1.5) | |||
| Tx_YN (%) | FALSE | 80 ( 17.2) | 0 ( 0.0) | <0.001 | |
| TRUE | 363 ( 77.9) | 193 ( 95.5) | |||
| NA | 23 ( 4.9) | 9 ( 4.5) | |||
| mets_at_dx (%) | Bone | 8 ( 1.7) | 2 ( 1.0) | NaN | |
| Brain | 0 ( 0.0) | 0 ( 0.0) | |||
| Liver | 1 ( 0.2) | 0 ( 0.0) | |||
| Lung | 1 ( 0.2) | 3 ( 1.5) | |||
| None/Other/Unk/NA | 456 ( 97.9) | 197 ( 97.5) | |||
| MEDICAID_EXPN_CODE (%) | Non-Expansion State | 189 ( 40.6) | 71 ( 35.1) | 0.605 | |
| Jan 2014 Expansion States | 138 ( 29.6) | 67 ( 33.2) | |||
| Early Expansion States (2010-13) | 55 ( 11.8) | 21 ( 10.4) | |||
| Late Expansion States (> Jan 2014) | 61 ( 13.1) | 32 ( 15.8) | |||
| Suppressed for Ages 0 - 39 | 23 ( 4.9) | 11 ( 5.4) |
p_table(data,
vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY", "AGE", "AGE_F", "AGE_40",
"SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F",
"INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
"SITE_TEXT", "BEHAVIOR", "GRADE_F",
"DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
"TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
"TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
"DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
"READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
"PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS",
"LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
"SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN",
"CHEMO_YN", "IMMUNO_YN", "Tx_YN","mets_at_dx",
"MEDICAID_EXPN_CODE"),
strata = "CHEMO_YN")
| level | No | Yes | Ukn | p | test | |
|---|---|---|---|---|---|---|
| n | 563 | 74 | 38 | |||
| FACILITY_TYPE_F (%) | Community Cancer Program | 71 ( 12.6) | 7 ( 9.5) | 3 ( 7.9) | <0.001 | |
| Comprehensive Comm Ca Program | 244 ( 43.3) | 32 ( 43.2) | 20 ( 52.6) | |||
| Academic/Research Program | 154 ( 27.4) | 13 ( 17.6) | 6 ( 15.8) | |||
| Integrated Network Ca Program | 76 ( 13.5) | 9 ( 12.2) | 5 ( 13.2) | |||
| NA | 18 ( 3.2) | 13 ( 17.6) | 4 ( 10.5) | |||
| FACILITY_LOCATION_F (%) | New England | 25 ( 4.4) | 5 ( 6.8) | 1 ( 2.6) | 0.002 | |
| Middle Atlantic | 78 ( 13.9) | 6 ( 8.1) | 9 ( 23.7) | |||
| South Atlantic | 124 ( 22.0) | 13 ( 17.6) | 6 ( 15.8) | |||
| East North Central | 117 ( 20.8) | 12 ( 16.2) | 5 ( 13.2) | |||
| East South Central | 39 ( 6.9) | 4 ( 5.4) | 1 ( 2.6) | |||
| West North Central | 50 ( 8.9) | 6 ( 8.1) | 3 ( 7.9) | |||
| West South Central | 46 ( 8.2) | 9 ( 12.2) | 5 ( 13.2) | |||
| Mountain | 30 ( 5.3) | 3 ( 4.1) | 2 ( 5.3) | |||
| Pacific | 36 ( 6.4) | 3 ( 4.1) | 2 ( 5.3) | |||
| NA | 18 ( 3.2) | 13 ( 17.6) | 4 ( 10.5) | |||
| FACILITY_GEOGRAPHY (%) | Northeast | 103 ( 18.3) | 11 ( 14.9) | 10 ( 26.3) | <0.001 | |
| South | 170 ( 30.2) | 22 ( 29.7) | 11 ( 28.9) | |||
| Midwest | 206 ( 36.6) | 22 ( 29.7) | 9 ( 23.7) | |||
| West | 66 ( 11.7) | 6 ( 8.1) | 4 ( 10.5) | |||
| NA | 18 ( 3.2) | 13 ( 17.6) | 4 ( 10.5) | |||
| AGE (mean (sd)) | 67.39 (14.49) | 52.42 (12.10) | 64.11 (15.57) | <0.001 | ||
| AGE_F (%) | (0,54] | 107 ( 19.0) | 42 ( 56.8) | 12 ( 31.6) | <0.001 | |
| (54,64] | 126 ( 22.4) | 19 ( 25.7) | 4 ( 10.5) | |||
| (64,74] | 128 ( 22.7) | 11 ( 14.9) | 10 ( 26.3) | |||
| (74,100] | 202 ( 35.9) | 2 ( 2.7) | 12 ( 31.6) | |||
| AGE_40 (%) | (0,40] | 20 ( 3.6) | 13 ( 17.6) | 5 ( 13.2) | <0.001 | |
| (40,100] | 543 ( 96.4) | 61 ( 82.4) | 33 ( 86.8) | |||
| SEX_F (%) | Male | 15 ( 2.7) | 3 ( 4.1) | 1 ( 2.6) | 0.792 | |
| Female | 548 ( 97.3) | 71 ( 95.9) | 37 ( 97.4) | |||
| RACE_F (%) | White | 487 ( 86.5) | 55 ( 74.3) | 28 ( 73.7) | 0.036 | |
| Black | 54 ( 9.6) | 16 ( 21.6) | 7 ( 18.4) | |||
| Other/Unk | 12 ( 2.1) | 2 ( 2.7) | 2 ( 5.3) | |||
| Asian | 10 ( 1.8) | 1 ( 1.4) | 1 ( 2.6) | |||
| HISPANIC (%) | No | 494 ( 87.7) | 62 ( 83.8) | 31 ( 81.6) | 0.576 | |
| Yes | 20 ( 3.6) | 5 ( 6.8) | 2 ( 5.3) | |||
| Unknown | 49 ( 8.7) | 7 ( 9.5) | 5 ( 13.2) | |||
| INSURANCE_F (%) | Private | 212 ( 37.7) | 47 ( 63.5) | 14 ( 36.8) | <0.001 | |
| None | 19 ( 3.4) | 3 ( 4.1) | 2 ( 5.3) | |||
| Medicaid | 25 ( 4.4) | 8 ( 10.8) | 2 ( 5.3) | |||
| Medicare | 288 ( 51.2) | 13 ( 17.6) | 19 ( 50.0) | |||
| Other Government | 6 ( 1.1) | 2 ( 2.7) | 0 ( 0.0) | |||
| Unknown | 13 ( 2.3) | 1 ( 1.4) | 1 ( 2.6) | |||
| INCOME_F (%) | Less than $38,000 | 106 ( 18.8) | 16 ( 21.6) | 8 ( 21.1) | 0.455 | |
| $38,000 - $47,999 | 119 ( 21.1) | 16 ( 21.6) | 9 ( 23.7) | |||
| $48,000 - $62,999 | 155 ( 27.5) | 17 ( 23.0) | 6 ( 15.8) | |||
| $63,000 + | 180 ( 32.0) | 23 ( 31.1) | 15 ( 39.5) | |||
| NA | 3 ( 0.5) | 2 ( 2.7) | 0 ( 0.0) | |||
| EDUCATION_F (%) | 21% or more | 75 ( 13.3) | 15 ( 20.3) | 9 ( 23.7) | 0.412 | |
| 13 - 20.9% | 140 ( 24.9) | 14 ( 18.9) | 10 ( 26.3) | |||
| 7 - 12.9% | 189 ( 33.6) | 25 ( 33.8) | 8 ( 21.1) | |||
| Less than 7% | 156 ( 27.7) | 19 ( 25.7) | 11 ( 28.9) | |||
| NA | 3 ( 0.5) | 1 ( 1.4) | 0 ( 0.0) | |||
| U_R_F (%) | Metro | 464 ( 82.4) | 60 ( 81.1) | 33 ( 86.8) | 0.561 | |
| Urban | 73 ( 13.0) | 12 ( 16.2) | 3 ( 7.9) | |||
| Rural | 12 ( 2.1) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 14 ( 2.5) | 2 ( 2.7) | 2 ( 5.3) | |||
| CROWFLY (mean (sd)) | 24.76 (94.40) | 17.73 (21.82) | 59.73 (310.83) | 0.149 | ||
| CDCC_TOTAL_BEST (%) | 0 | 462 ( 82.1) | 65 ( 87.8) | 31 ( 81.6) | 0.535 | |
| 1 | 67 ( 11.9) | 7 ( 9.5) | 6 ( 15.8) | |||
| 2 | 22 ( 3.9) | 0 ( 0.0) | 1 ( 2.6) | |||
| 3 | 12 ( 2.1) | 2 ( 2.7) | 0 ( 0.0) | |||
| SITE_TEXT (%) | C00.0 External Lip: Upper NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| C00.1 External Lip: Lower NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.2 External Lip: NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.3 Lip: Upper Mucosa | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.4 Lip: Lower Mucosa | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.5 Lip: Mucosa NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.6 Lip: Commissure | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.8 Lip: Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.9 Lip NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C01.9 Tongue: Base NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.0 Tongue: Dorsal NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.1 Tongue: Border, Tip | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.2 Tongue: Ventral NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.3 Tongue: Anterior NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.4 Lingual Tonsil | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.8 Tongue: Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.9 Tongue: NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.0 Gum: Upper | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.1 Gum: Lower | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.9 Gum NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.0 Mouth: Anterior Floor | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.1 Mouth: Lateral Floor | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.9 Floor of Mouth NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.0 Hard Palate | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.1 Soft Palate NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.2 Uvula | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.8 Palate: Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.9 Palate NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.0 Cheek Mucosa | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.1 Mouth: Vestibule | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.2 Retromolar Area | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.8 Mouth: Other Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.9 Mouth NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C07.9 Parotid Gland | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C09.8 Tonsil: Overlapping | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C09.9 Tonsil NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C11.1 Nasopharynx: Poster Wall | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C14.2 Waldeyer Ring | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C30.0 Nasal Cavity | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C37.9 Thymus | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.0 Blood | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.2 Spleen | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.4 Hematopoietic NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.0 Skin of lip, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.1 Eyelid | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.2 External ear | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.3 Skin of ear and unspecified parts of face | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.4 Skin of scalp and neck | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.5 Skin of trunk | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.6 Skin of upper limb and shoulder | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.7 Skin of lower limb and hip | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.8 Overlapping lesion of skin | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.9 Skin, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C50.0 Nipple | 415 ( 73.7) | 8 ( 10.8) | 25 ( 65.8) | |||
| C51.0 Labium majus | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.1 Labium minus | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.2 Clitoris | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.8 Overlapping lesion of vulva | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.9 Vulva, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C52.9 Vagina, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.0 Prepuce | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.1 Glans penis | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.2 Body of penis | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.8 Overlapping lesion of penis | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.9 Penis | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C63.2 Scrotum, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.0 Lymph Nodes: HeadFaceNeck | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.1 Intrathoracic Lymph Nodes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.2 Intra-abdominal LymphNodes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.3 Lymph Nodes of axilla or arm | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.4 Lymph Nodes: Leg | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.5 Pelvic Lymph Nodes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.8 Lymph Nodes: multiple region | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.9 Lymph Node NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 148 ( 26.3) | 66 ( 89.2) | 13 ( 34.2) | |||
| BEHAVIOR (%) | 2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| 3 | 563 (100.0) | 74 (100.0) | 38 (100.0) | |||
| GRADE_F (%) | Gr I: Well Diff | 18 ( 3.2) | 3 ( 4.1) | 1 ( 2.6) | NaN | |
| Gr II: Mod Diff | 38 ( 6.7) | 16 ( 21.6) | 1 ( 2.6) | |||
| Gr III: Poor Diff | 58 ( 10.3) | 38 ( 51.4) | 10 ( 26.3) | |||
| Gr IV: Undiff/Anaplastic | 2 ( 0.4) | 0 ( 0.0) | 0 ( 0.0) | |||
| 5 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 6 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 7 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 8 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA/Unkown | 447 ( 79.4) | 17 ( 23.0) | 26 ( 68.4) | |||
| DX_STAGING_PROC_DAYS (mean (sd)) | 1.10 (7.32) | 6.38 (22.02) | 0.00 (0.00) | 0.001 | ||
| TNM_CLIN_T (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 4 ( 0.7) | 1 ( 1.4) | 0 ( 0.0) | |||
| c1 | 27 ( 4.8) | 7 ( 9.5) | 3 ( 7.9) | |||
| c1A | 10 ( 1.8) | 2 ( 2.7) | 0 ( 0.0) | |||
| c1B | 8 ( 1.4) | 0 ( 0.0) | 1 ( 2.6) | |||
| c1C | 12 ( 2.1) | 3 ( 4.1) | 0 ( 0.0) | |||
| c1MI | 4 ( 0.7) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2 | 13 ( 2.3) | 8 ( 10.8) | 0 ( 0.0) | |||
| c2A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2D | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3 | 6 ( 1.1) | 12 ( 16.2) | 0 ( 0.0) | |||
| c3A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c4 | 5 ( 0.9) | 5 ( 6.8) | 0 ( 0.0) | |||
| c4A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c4B | 6 ( 1.1) | 3 ( 4.1) | 1 ( 2.6) | |||
| c4C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c4D | 1 ( 0.2) | 2 ( 2.7) | 0 ( 0.0) | |||
| cX | 146 ( 25.9) | 28 ( 37.8) | 14 ( 36.8) | |||
| pA | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| pIS | 286 ( 50.8) | 3 ( 4.1) | 18 ( 47.4) | |||
| NA | 35 ( 6.2) | 0 ( 0.0) | 1 ( 2.6) | |||
| TNM_CLIN_N (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 399 ( 70.9) | 30 ( 40.5) | 20 ( 52.6) | |||
| c1 | 14 ( 2.5) | 12 ( 16.2) | 0 ( 0.0) | |||
| c1A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c1B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2 | 3 ( 0.5) | 1 ( 1.4) | 2 ( 5.3) | |||
| c2A | 1 ( 0.2) | 1 ( 1.4) | 0 ( 0.0) | |||
| c2B | 1 ( 0.2) | 0 ( 0.0) | 0 ( 0.0) | |||
| c2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3 | 1 ( 0.2) | 2 ( 2.7) | 0 ( 0.0) | |||
| c3A | 0 ( 0.0) | 1 ( 1.4) | 0 ( 0.0) | |||
| c3B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c3C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c4 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| cX | 120 ( 21.3) | 26 ( 35.1) | 16 ( 42.1) | |||
| NA | 24 ( 4.3) | 1 ( 1.4) | 0 ( 0.0) | |||
| TNM_CLIN_M (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 519 ( 92.2) | 62 ( 83.8) | 33 ( 86.8) | |||
| c0I+ | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c1 | 13 ( 2.3) | 11 ( 14.9) | 2 ( 5.3) | |||
| c1A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c1B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| c1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 31 ( 5.5) | 1 ( 1.4) | 3 ( 7.9) | |||
| TNM_CLIN_STAGE_GROUP (%) | 0 | 312 ( 55.4) | 3 ( 4.1) | 18 ( 47.4) | NaN | |
| 1 | 33 ( 5.9) | 1 ( 1.4) | 5 ( 13.2) | |||
| 1A | 25 ( 4.4) | 7 ( 9.5) | 0 ( 0.0) | |||
| 1B | 1 ( 0.2) | 0 ( 0.0) | 0 ( 0.0) | |||
| 1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 2A | 13 ( 2.3) | 10 ( 13.5) | 0 ( 0.0) | |||
| 2B | 7 ( 1.2) | 6 ( 8.1) | 0 ( 0.0) | |||
| 2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 3 | 1 ( 0.2) | 1 ( 1.4) | 0 ( 0.0) | |||
| 3A | 3 ( 0.5) | 4 ( 5.4) | 0 ( 0.0) | |||
| 3B | 6 ( 1.1) | 6 ( 8.1) | 0 ( 0.0) | |||
| 3C | 1 ( 0.2) | 2 ( 2.7) | 0 ( 0.0) | |||
| 4 | 14 ( 2.5) | 11 ( 14.9) | 2 ( 5.3) | |||
| 4A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A1 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 99 | 147 ( 26.1) | 23 ( 31.1) | 13 ( 34.2) | |||
| TNM_PATH_T (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 5 ( 0.9) | 3 ( 4.1) | 0 ( 0.0) | |||
| p1 | 11 ( 2.0) | 1 ( 1.4) | 1 ( 2.6) | |||
| p1A | 15 ( 2.7) | 2 ( 2.7) | 0 ( 0.0) | |||
| p1B | 4 ( 0.7) | 6 ( 8.1) | 1 ( 2.6) | |||
| p1C | 13 ( 2.3) | 10 ( 13.5) | 1 ( 2.6) | |||
| p1MI | 6 ( 1.1) | 1 ( 1.4) | 0 ( 0.0) | |||
| p2 | 5 ( 0.9) | 7 ( 9.5) | 0 ( 0.0) | |||
| p2A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2D | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p3 | 0 ( 0.0) | 2 ( 2.7) | 0 ( 0.0) | |||
| p3A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p3B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4 | 1 ( 0.2) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4B | 3 ( 0.5) | 2 ( 2.7) | 0 ( 0.0) | |||
| p4C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4D | 1 ( 0.2) | 2 ( 2.7) | 0 ( 0.0) | |||
| pA | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| pIS | 241 ( 42.8) | 2 ( 2.7) | 18 ( 47.4) | |||
| pX | 208 ( 36.9) | 32 ( 43.2) | 13 ( 34.2) | |||
| NA | 50 ( 8.9) | 4 ( 5.4) | 4 ( 10.5) | |||
| TNM_PATH_N (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 201 ( 35.7) | 17 ( 23.0) | 15 ( 39.5) | |||
| p0I- | 24 ( 4.3) | 5 ( 6.8) | 1 ( 2.6) | |||
| p0I+ | 1 ( 0.2) | 1 ( 1.4) | 0 ( 0.0) | |||
| p0M- | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p0M+ | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1 | 0 ( 0.0) | 7 ( 9.5) | 0 ( 0.0) | |||
| p1A | 3 ( 0.5) | 6 ( 8.1) | 0 ( 0.0) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1MI | 2 ( 0.4) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2 | 0 ( 0.0) | 2 ( 2.7) | 0 ( 0.0) | |||
| p2A | 2 ( 0.4) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p3 | 1 ( 0.2) | 1 ( 1.4) | 0 ( 0.0) | |||
| p3A | 0 ( 0.0) | 3 ( 4.1) | 0 ( 0.0) | |||
| p3B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p3C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p4 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| pX | 258 ( 45.8) | 28 ( 37.8) | 18 ( 47.4) | |||
| NA | 71 ( 12.6) | 4 ( 5.4) | 4 ( 10.5) | |||
| TNM_PATH_M (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1 | 2 ( 0.4) | 6 ( 8.1) | 0 ( 0.0) | |||
| p1A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| p1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| pX | 285 ( 50.6) | 37 ( 50.0) | 22 ( 57.9) | |||
| NA | 276 ( 49.0) | 31 ( 41.9) | 16 ( 42.1) | |||
| TNM_PATH_STAGE_GROUP (%) | 0 | 279 ( 49.6) | 2 ( 2.7) | 17 ( 44.7) | NaN | |
| 1 | 45 ( 8.0) | 6 ( 8.1) | 4 ( 10.5) | |||
| 1A | 18 ( 3.2) | 8 ( 10.8) | 0 ( 0.0) | |||
| 1B | 1 ( 0.2) | 1 ( 1.4) | 0 ( 0.0) | |||
| 1C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 2 | 1 ( 0.2) | 2 ( 2.7) | 0 ( 0.0) | |||
| 2A | 11 ( 2.0) | 10 ( 13.5) | 0 ( 0.0) | |||
| 2B | 2 ( 0.4) | 6 ( 8.1) | 0 ( 0.0) | |||
| 2C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 3 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 3A | 1 ( 0.2) | 3 ( 4.1) | 0 ( 0.0) | |||
| 3B | 4 ( 0.7) | 2 ( 2.7) | 0 ( 0.0) | |||
| 3C | 2 ( 0.4) | 3 ( 4.1) | 0 ( 0.0) | |||
| 4 | 2 ( 0.4) | 7 ( 9.5) | 0 ( 0.0) | |||
| 4A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A1 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4B | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 4C | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| 99 | 164 ( 29.1) | 20 ( 27.0) | 14 ( 36.8) | |||
| NA | 33 ( 5.9) | 4 ( 5.4) | 3 ( 7.9) | |||
| DX_RX_STARTED_DAYS (mean (sd)) | 36.02 (35.73) | 34.75 (27.98) | 27.34 (35.79) | 0.423 | ||
| DX_SURG_STARTED_DAYS (mean (sd)) | 34.31 (29.50) | 80.57 (75.72) | 22.48 (25.84) | <0.001 | ||
| DX_DEFSURG_STARTED_DAYS (mean (sd)) | 40.62 (37.04) | 96.98 (75.88) | 33.67 (24.15) | <0.001 | ||
| MARGINS (%) | No Residual | 434 ( 77.1) | 50 ( 67.6) | 27 ( 71.1) | NaN | |
| Residual, NOS | 8 ( 1.4) | 2 ( 2.7) | 0 ( 0.0) | |||
| Microscopic Resid | 6 ( 1.1) | 5 ( 6.8) | 0 ( 0.0) | |||
| Macroscopic Resid | 1 ( 0.2) | 1 ( 1.4) | 1 ( 2.6) | |||
| Not evaluable | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| No surg | 99 ( 17.6) | 14 ( 18.9) | 9 ( 23.7) | |||
| Unknown | 15 ( 2.7) | 2 ( 2.7) | 1 ( 2.6) | |||
| MARGINS_YN (%) | No | 434 ( 77.1) | 50 ( 67.6) | 27 ( 71.1) | 0.008 | |
| Yes | 15 ( 2.7) | 8 ( 10.8) | 1 ( 2.6) | |||
| No surg/Unk/NA | 114 ( 20.2) | 16 ( 21.6) | 10 ( 26.3) | |||
| SURG_DISCHARGE_DAYS (mean (sd)) | 1.05 (4.35) | 1.00 (1.59) | 0.35 (0.75) | 0.695 | ||
| READM_HOSP_30_DAYS_F (%) | No_Surg_or_No_Readmit | 529 ( 94.0) | 68 ( 91.9) | 30 ( 78.9) | 0.001 | |
| Unplan_Readmit_Same | 11 ( 2.0) | 2 ( 2.7) | 2 ( 5.3) | |||
| Plan_Readmit_Same | 14 ( 2.5) | 1 ( 1.4) | 3 ( 7.9) | |||
| PlanUnplan_Same | 0 ( 0.0) | 0 ( 0.0) | 1 ( 2.6) | |||
| 9 | 9 ( 1.6) | 3 ( 4.1) | 2 ( 5.3) | |||
| RX_SUMM_RADIATION_F (%) | None | 409 ( 72.6) | 34 ( 45.9) | 22 ( 57.9) | NaN | |
| Beam Radiation | 152 ( 27.0) | 39 ( 52.7) | 9 ( 23.7) | |||
| Radioactive Implants | 1 ( 0.2) | 1 ( 1.4) | 0 ( 0.0) | |||
| Radioisotopes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Beam + Imp or Isotopes | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Radiation, NOS | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 1 ( 0.2) | 0 ( 0.0) | 7 ( 18.4) | |||
| PUF_30_DAY_MORT_CD_F (%) | Alive_30 | 456 ( 81.0) | 59 ( 79.7) | 27 ( 71.1) | 0.730 | |
| Dead_30 | 1 ( 0.2) | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 4 ( 0.7) | 1 ( 1.4) | 1 ( 2.6) | |||
| NA | 102 ( 18.1) | 14 ( 18.9) | 10 ( 26.3) | |||
| PUF_90_DAY_MORT_CD_F (%) | Alive_90 | 450 ( 79.9) | 59 ( 79.7) | 27 ( 71.1) | 0.857 | |
| Dead_90 | 3 ( 0.5) | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 8 ( 1.4) | 1 ( 1.4) | 1 ( 2.6) | |||
| NA | 102 ( 18.1) | 14 ( 18.9) | 10 ( 26.3) | |||
| DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) | 59.34 (42.73) | 58.56 (42.82) | 58.09 (44.79) | 0.976 | ||
| LYMPH_VASCULAR_INVASION_F (%) | Neg_LymphVasc_Inv | 139 ( 24.7) | 17 ( 23.0) | 6 ( 15.8) | NaN | |
| Pos_LumphVasc_Inv | 2 ( 0.4) | 8 ( 10.8) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 136 ( 24.2) | 8 ( 10.8) | 10 ( 26.3) | |||
| NA | 286 ( 50.8) | 41 ( 55.4) | 22 ( 57.9) | |||
| RX_HOSP_SURG_APPR_2010_F (%) | No_Surg | 79 ( 14.0) | 11 ( 14.9) | 6 ( 15.8) | NaN | |
| Robot_Assist | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Robot_to_Open | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Endo_Lap | 0 ( 0.0) | 1 ( 1.4) | 0 ( 0.0) | |||
| Endo_Lap_to_Open | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Open_Unknown | 198 ( 35.2) | 21 ( 28.4) | 10 ( 26.3) | |||
| Unknown | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 286 ( 50.8) | 41 ( 55.4) | 22 ( 57.9) | |||
| SURG_RAD_SEQ (%) | Surg Alone | 321 ( 57.0) | 25 ( 33.8) | 17 ( 44.7) | NaN | |
| Surg then Rad | 141 ( 25.0) | 33 ( 44.6) | 7 ( 18.4) | |||
| Rad Alone | 12 ( 2.1) | 5 ( 6.8) | 2 ( 5.3) | |||
| No Treatment | 81 ( 14.4) | 9 ( 12.2) | 5 ( 13.2) | |||
| Other | 8 ( 1.4) | 0 ( 0.0) | 7 ( 18.4) | |||
| Rad before and after Surg | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad then Surg | 0 ( 0.0) | 2 ( 2.7) | 0 ( 0.0) | |||
| SURG_RAD_SEQ_C (%) | Surg, No rad, No Chemo | 321 ( 57.0) | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| Surg then Rad, No Chemo | 141 ( 25.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Surg then Rad, Yes Chemo | 0 ( 0.0) | 33 ( 44.6) | 0 ( 0.0) | |||
| Surg, No rad, Yes Chemo | 0 ( 0.0) | 25 ( 33.8) | 0 ( 0.0) | |||
| No Surg, No Rad, Yes Chemo | 0 ( 0.0) | 9 ( 12.2) | 0 ( 0.0) | |||
| No Surg, No Rad, No Chemo | 81 ( 14.4) | 0 ( 0.0) | 0 ( 0.0) | |||
| Other | 8 ( 1.4) | 0 ( 0.0) | 38 (100.0) | |||
| Rad, No Surg, Yes Chemo | 0 ( 0.0) | 5 ( 6.8) | 0 ( 0.0) | |||
| Rad, No Surg, No Chemo | 12 ( 2.1) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad then Surg, Yes Chemo | 0 ( 0.0) | 2 ( 2.7) | 0 ( 0.0) | |||
| Rad then Surg, No Chemo | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad before and after Surg, Yes Chemo | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad before and after Surg, No Chemo | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| T_SIZE (%) | No Tumor | 3 ( 0.5) | 1 ( 1.4) | 0 ( 0.0) | <0.001 | |
| Microscopic focus | 16 ( 2.8) | 1 ( 1.4) | 0 ( 0.0) | |||
| < 1 cm | 74 ( 13.1) | 4 ( 5.4) | 6 ( 15.8) | |||
| 1-2 cm | 66 ( 11.7) | 20 ( 27.0) | 5 ( 13.2) | |||
| 2-3 cm | 41 ( 7.3) | 11 ( 14.9) | 1 ( 2.6) | |||
| 3-4 cm | 21 ( 3.7) | 6 ( 8.1) | 0 ( 0.0) | |||
| 4-5 cm | 5 ( 0.9) | 5 ( 6.8) | 1 ( 2.6) | |||
| 5-6 cm | 4 ( 0.7) | 4 ( 5.4) | 0 ( 0.0) | |||
| >6 cm | 14 ( 2.5) | 13 ( 17.6) | 4 ( 10.5) | |||
| NA_unk | 319 ( 56.7) | 9 ( 12.2) | 21 ( 55.3) | |||
| SURGERY_YN (%) | No | 93 ( 16.5) | 14 ( 18.9) | 8 ( 21.1) | 0.175 | |
| Ukn | 7 ( 1.2) | 0 ( 0.0) | 2 ( 5.3) | |||
| Yes | 463 ( 82.2) | 60 ( 81.1) | 28 ( 73.7) | |||
| RADIATION_YN (%) | No | 409 ( 72.6) | 34 ( 45.9) | 23 ( 60.5) | <0.001 | |
| Yes | 153 ( 27.2) | 40 ( 54.1) | 9 ( 23.7) | |||
| NA | 1 ( 0.2) | 0 ( 0.0) | 6 ( 15.8) | |||
| CHEMO_YN (%) | No | 563 (100.0) | 0 ( 0.0) | 0 ( 0.0) | <0.001 | |
| Yes | 0 ( 0.0) | 74 (100.0) | 0 ( 0.0) | |||
| Ukn | 0 ( 0.0) | 0 ( 0.0) | 38 (100.0) | |||
| IMMUNO_YN (%) | No | 562 ( 99.8) | 68 ( 91.9) | 27 ( 71.1) | <0.001 | |
| Yes | 0 ( 0.0) | 5 ( 6.8) | 0 ( 0.0) | |||
| Ukn | 1 ( 0.2) | 1 ( 1.4) | 11 ( 28.9) | |||
| Tx_YN (%) | FALSE | 80 ( 14.2) | 0 ( 0.0) | 0 ( 0.0) | <0.001 | |
| TRUE | 483 ( 85.8) | 74 (100.0) | 0 ( 0.0) | |||
| NA | 0 ( 0.0) | 0 ( 0.0) | 38 (100.0) | |||
| mets_at_dx (%) | Bone | 4 ( 0.7) | 6 ( 8.1) | 0 ( 0.0) | NaN | |
| Brain | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | |||
| Liver | 0 ( 0.0) | 1 ( 1.4) | 0 ( 0.0) | |||
| Lung | 3 ( 0.5) | 0 ( 0.0) | 1 ( 2.6) | |||
| None/Other/Unk/NA | 556 ( 98.8) | 67 ( 90.5) | 37 ( 97.4) | |||
| MEDICAID_EXPN_CODE (%) | Non-Expansion State | 225 ( 40.0) | 26 ( 35.1) | 10 ( 26.3) | <0.001 | |
| Jan 2014 Expansion States | 174 ( 30.9) | 24 ( 32.4) | 9 ( 23.7) | |||
| Early Expansion States (2010-13) | 61 ( 10.8) | 7 ( 9.5) | 9 ( 23.7) | |||
| Late Expansion States (> Jan 2014) | 85 ( 15.1) | 4 ( 5.4) | 6 ( 15.8) | |||
| Suppressed for Ages 0 - 39 | 18 ( 3.2) | 13 ( 17.6) | 4 ( 10.5) |
p_table(data,
vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY", "AGE", "AGE_F", "AGE_40",
"SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F",
"INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
"SITE_TEXT", "BEHAVIOR", "GRADE_F",
"DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
"TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
"TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
"DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
"READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
"PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS",
"LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
"SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN",
"CHEMO_YN", "mets_at_dx", "IMMUNO_YN", "Tx_YN",
"MEDICAID_EXPN_CODE"),
strata = "Tx_YN")no non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -InfVariable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.| level | FALSE | TRUE | p | test | |
|---|---|---|---|---|---|
| n | 80 | 557 | |||
| FACILITY_TYPE_F (%) | Community Cancer Program | 14 ( 17.5) | 64 ( 11.5) | 0.294 | |
| Comprehensive Comm Ca Program | 36 ( 45.0) | 240 ( 43.1) | |||
| Academic/Research Program | 20 ( 25.0) | 147 ( 26.4) | |||
| Integrated Network Ca Program | 9 ( 11.2) | 76 ( 13.6) | |||
| NA | 1 ( 1.2) | 30 ( 5.4) | |||
| FACILITY_LOCATION_F (%) | New England | 8 ( 10.0) | 22 ( 3.9) | 0.012 | |
| Middle Atlantic | 18 ( 22.5) | 66 ( 11.8) | |||
| South Atlantic | 15 ( 18.8) | 122 ( 21.9) | |||
| East North Central | 16 ( 20.0) | 113 ( 20.3) | |||
| East South Central | 5 ( 6.2) | 38 ( 6.8) | |||
| West North Central | 7 ( 8.8) | 49 ( 8.8) | |||
| West South Central | 8 ( 10.0) | 47 ( 8.4) | |||
| Mountain | 0 ( 0.0) | 33 ( 5.9) | |||
| Pacific | 2 ( 2.5) | 37 ( 6.6) | |||
| NA | 1 ( 1.2) | 30 ( 5.4) | |||
| FACILITY_GEOGRAPHY (%) | Northeast | 26 ( 32.5) | 88 ( 15.8) | 0.001 | |
| South | 23 ( 28.7) | 169 ( 30.3) | |||
| Midwest | 28 ( 35.0) | 200 ( 35.9) | |||
| West | 2 ( 2.5) | 70 ( 12.6) | |||
| NA | 1 ( 1.2) | 30 ( 5.4) | |||
| AGE (mean (sd)) | 74.69 (14.06) | 64.35 (14.71) | <0.001 | ||
| AGE_F (%) | (0,54] | 7 ( 8.8) | 142 ( 25.5) | <0.001 | |
| (54,64] | 17 ( 21.2) | 128 ( 23.0) | |||
| (64,74] | 9 ( 11.2) | 130 ( 23.3) | |||
| (74,100] | 47 ( 58.8) | 157 ( 28.2) | |||
| AGE_40 (%) | (0,40] | 1 ( 1.2) | 32 ( 5.7) | 0.154 | |
| (40,100] | 79 ( 98.8) | 525 ( 94.3) | |||
| SEX_F (%) | Male | 2 ( 2.5) | 16 ( 2.9) | 1.000 | |
| Female | 78 ( 97.5) | 541 ( 97.1) | |||
| RACE_F (%) | White | 63 ( 78.8) | 479 ( 86.0) | 0.249 | |
| Black | 14 ( 17.5) | 56 ( 10.1) | |||
| Other/Unk | 2 ( 2.5) | 12 ( 2.2) | |||
| Asian | 1 ( 1.2) | 10 ( 1.8) | |||
| HISPANIC (%) | No | 72 ( 90.0) | 484 ( 86.9) | 0.685 | |
| Yes | 3 ( 3.8) | 22 ( 3.9) | |||
| Unknown | 5 ( 6.2) | 51 ( 9.2) | |||
| INSURANCE_F (%) | Private | 17 ( 21.2) | 242 ( 43.4) | 0.001 | |
| None | 5 ( 6.2) | 17 ( 3.1) | |||
| Medicaid | 5 ( 6.2) | 28 ( 5.0) | |||
| Medicare | 48 ( 60.0) | 253 ( 45.4) | |||
| Other Government | 0 ( 0.0) | 8 ( 1.4) | |||
| Unknown | 5 ( 6.2) | 9 ( 1.6) | |||
| INCOME_F (%) | Less than $38,000 | 18 ( 22.5) | 104 ( 18.7) | 0.888 | |
| $38,000 - $47,999 | 16 ( 20.0) | 119 ( 21.4) | |||
| $48,000 - $62,999 | 22 ( 27.5) | 150 ( 26.9) | |||
| $63,000 + | 23 ( 28.7) | 180 ( 32.3) | |||
| NA | 1 ( 1.2) | 4 ( 0.7) | |||
| EDUCATION_F (%) | 21% or more | 17 ( 21.2) | 73 ( 13.1) | 0.257 | |
| 13 - 20.9% | 20 ( 25.0) | 134 ( 24.1) | |||
| 7 - 12.9% | 25 ( 31.2) | 189 ( 33.9) | |||
| Less than 7% | 17 ( 21.2) | 158 ( 28.4) | |||
| NA | 1 ( 1.2) | 3 ( 0.5) | |||
| U_R_F (%) | Metro | 68 ( 85.0) | 456 ( 81.9) | 0.901 | |
| Urban | 9 ( 11.2) | 76 ( 13.6) | |||
| Rural | 1 ( 1.2) | 11 ( 2.0) | |||
| NA | 2 ( 2.5) | 14 ( 2.5) | |||
| CROWFLY (mean (sd)) | 15.47 (20.54) | 25.16 (94.89) | 0.366 | ||
| CDCC_TOTAL_BEST (%) | 0 | 62 ( 77.5) | 465 ( 83.5) | 0.130 | |
| 1 | 9 ( 11.2) | 65 ( 11.7) | |||
| 2 | 5 ( 6.2) | 17 ( 3.1) | |||
| 3 | 4 ( 5.0) | 10 ( 1.8) | |||
| SITE_TEXT (%) | C00.0 External Lip: Upper NOS | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| C00.1 External Lip: Lower NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.2 External Lip: NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.3 Lip: Upper Mucosa | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.4 Lip: Lower Mucosa | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.5 Lip: Mucosa NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.6 Lip: Commissure | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.8 Lip: Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C00.9 Lip NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C01.9 Tongue: Base NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.0 Tongue: Dorsal NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.1 Tongue: Border, Tip | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.2 Tongue: Ventral NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.3 Tongue: Anterior NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.4 Lingual Tonsil | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.8 Tongue: Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C02.9 Tongue: NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.0 Gum: Upper | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.1 Gum: Lower | 0 ( 0.0) | 0 ( 0.0) | |||
| C03.9 Gum NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.0 Mouth: Anterior Floor | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.1 Mouth: Lateral Floor | 0 ( 0.0) | 0 ( 0.0) | |||
| C04.9 Floor of Mouth NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.0 Hard Palate | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.1 Soft Palate NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.2 Uvula | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.8 Palate: Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C05.9 Palate NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.0 Cheek Mucosa | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.1 Mouth: Vestibule | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.2 Retromolar Area | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.8 Mouth: Other Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C06.9 Mouth NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C07.9 Parotid Gland | 0 ( 0.0) | 0 ( 0.0) | |||
| C09.8 Tonsil: Overlapping | 0 ( 0.0) | 0 ( 0.0) | |||
| C09.9 Tonsil NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C11.1 Nasopharynx: Poster Wall | 0 ( 0.0) | 0 ( 0.0) | |||
| C14.2 Waldeyer Ring | 0 ( 0.0) | 0 ( 0.0) | |||
| C30.0 Nasal Cavity | 0 ( 0.0) | 0 ( 0.0) | |||
| C37.9 Thymus | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.0 Blood | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.2 Spleen | 0 ( 0.0) | 0 ( 0.0) | |||
| C42.4 Hematopoietic NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.0 Skin of lip, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.1 Eyelid | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.2 External ear | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.3 Skin of ear and unspecified parts of face | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.4 Skin of scalp and neck | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.5 Skin of trunk | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.6 Skin of upper limb and shoulder | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.7 Skin of lower limb and hip | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.8 Overlapping lesion of skin | 0 ( 0.0) | 0 ( 0.0) | |||
| C44.9 Skin, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C50.0 Nipple | 41 ( 51.2) | 382 ( 68.6) | |||
| C51.0 Labium majus | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.1 Labium minus | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.2 Clitoris | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.8 Overlapping lesion of vulva | 0 ( 0.0) | 0 ( 0.0) | |||
| C51.9 Vulva, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C52.9 Vagina, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.0 Prepuce | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.1 Glans penis | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.2 Body of penis | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.8 Overlapping lesion of penis | 0 ( 0.0) | 0 ( 0.0) | |||
| C60.9 Penis | 0 ( 0.0) | 0 ( 0.0) | |||
| C63.2 Scrotum, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.0 Lymph Nodes: HeadFaceNeck | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.1 Intrathoracic Lymph Nodes | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.2 Intra-abdominal LymphNodes | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.3 Lymph Nodes of axilla or arm | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.4 Lymph Nodes: Leg | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.5 Pelvic Lymph Nodes | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.8 Lymph Nodes: multiple region | 0 ( 0.0) | 0 ( 0.0) | |||
| C77.9 Lymph Node NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 39 ( 48.8) | 175 ( 31.4) | |||
| BEHAVIOR (%) | 2 | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| 3 | 80 (100.0) | 557 (100.0) | |||
| GRADE_F (%) | Gr I: Well Diff | 6 ( 7.5) | 15 ( 2.7) | NaN | |
| Gr II: Mod Diff | 4 ( 5.0) | 50 ( 9.0) | |||
| Gr III: Poor Diff | 5 ( 6.2) | 91 ( 16.3) | |||
| Gr IV: Undiff/Anaplastic | 0 ( 0.0) | 2 ( 0.4) | |||
| 5 | 0 ( 0.0) | 0 ( 0.0) | |||
| 6 | 0 ( 0.0) | 0 ( 0.0) | |||
| 7 | 0 ( 0.0) | 0 ( 0.0) | |||
| 8 | 0 ( 0.0) | 0 ( 0.0) | |||
| NA/Unkown | 65 ( 81.2) | 399 ( 71.6) | |||
| DX_STAGING_PROC_DAYS (mean (sd)) | 1.97 (7.63) | 1.73 (10.87) | 0.865 | ||
| TNM_CLIN_T (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 0 ( 0.0) | 5 ( 0.9) | |||
| c1 | 4 ( 5.0) | 30 ( 5.4) | |||
| c1A | 1 ( 1.2) | 11 ( 2.0) | |||
| c1B | 4 ( 5.0) | 4 ( 0.7) | |||
| c1C | 3 ( 3.8) | 12 ( 2.2) | |||
| c1MI | 0 ( 0.0) | 4 ( 0.7) | |||
| c2 | 5 ( 6.2) | 16 ( 2.9) | |||
| c2A | 0 ( 0.0) | 0 ( 0.0) | |||
| c2B | 0 ( 0.0) | 0 ( 0.0) | |||
| c2C | 0 ( 0.0) | 0 ( 0.0) | |||
| c2D | 0 ( 0.0) | 0 ( 0.0) | |||
| c3 | 3 ( 3.8) | 15 ( 2.7) | |||
| c3A | 0 ( 0.0) | 0 ( 0.0) | |||
| c3B | 0 ( 0.0) | 0 ( 0.0) | |||
| c4 | 4 ( 5.0) | 6 ( 1.1) | |||
| c4A | 0 ( 0.0) | 0 ( 0.0) | |||
| c4B | 2 ( 2.5) | 7 ( 1.3) | |||
| c4C | 0 ( 0.0) | 0 ( 0.0) | |||
| c4D | 1 ( 1.2) | 2 ( 0.4) | |||
| cX | 23 ( 28.7) | 151 ( 27.1) | |||
| pA | 0 ( 0.0) | 0 ( 0.0) | |||
| pIS | 23 ( 28.7) | 266 ( 47.8) | |||
| NA | 7 ( 8.8) | 28 ( 5.0) | |||
| TNM_CLIN_N (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 44 ( 55.0) | 385 ( 69.1) | |||
| c1 | 7 ( 8.8) | 19 ( 3.4) | |||
| c1A | 0 ( 0.0) | 0 ( 0.0) | |||
| c1B | 0 ( 0.0) | 0 ( 0.0) | |||
| c2 | 0 ( 0.0) | 4 ( 0.7) | |||
| c2A | 0 ( 0.0) | 2 ( 0.4) | |||
| c2B | 1 ( 1.2) | 0 ( 0.0) | |||
| c2C | 0 ( 0.0) | 0 ( 0.0) | |||
| c3 | 1 ( 1.2) | 2 ( 0.4) | |||
| c3A | 0 ( 0.0) | 1 ( 0.2) | |||
| c3B | 0 ( 0.0) | 0 ( 0.0) | |||
| c3C | 0 ( 0.0) | 0 ( 0.0) | |||
| c4 | 0 ( 0.0) | 0 ( 0.0) | |||
| cX | 20 ( 25.0) | 126 ( 22.6) | |||
| NA | 7 ( 8.8) | 18 ( 3.2) | |||
| TNM_CLIN_M (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| c0 | 61 ( 76.2) | 520 ( 93.4) | |||
| c0I+ | 0 ( 0.0) | 0 ( 0.0) | |||
| c1 | 8 ( 10.0) | 16 ( 2.9) | |||
| c1A | 0 ( 0.0) | 0 ( 0.0) | |||
| c1B | 0 ( 0.0) | 0 ( 0.0) | |||
| c1C | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 11 ( 13.8) | 21 ( 3.8) | |||
| TNM_CLIN_STAGE_GROUP (%) | 0 | 27 ( 33.8) | 288 ( 51.7) | NaN | |
| 1 | 6 ( 7.5) | 28 ( 5.0) | |||
| 1A | 4 ( 5.0) | 28 ( 5.0) | |||
| 1B | 0 ( 0.0) | 1 ( 0.2) | |||
| 1C | 0 ( 0.0) | 0 ( 0.0) | |||
| 2 | 0 ( 0.0) | 0 ( 0.0) | |||
| 2A | 2 ( 2.5) | 21 ( 3.8) | |||
| 2B | 4 ( 5.0) | 9 ( 1.6) | |||
| 2C | 0 ( 0.0) | 0 ( 0.0) | |||
| 3 | 1 ( 1.2) | 1 ( 0.2) | |||
| 3A | 1 ( 1.2) | 6 ( 1.1) | |||
| 3B | 3 ( 3.8) | 9 ( 1.6) | |||
| 3C | 1 ( 1.2) | 2 ( 0.4) | |||
| 4 | 9 ( 11.2) | 16 ( 2.9) | |||
| 4A | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A1 | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A2 | 0 ( 0.0) | 0 ( 0.0) | |||
| 4B | 0 ( 0.0) | 0 ( 0.0) | |||
| 4C | 0 ( 0.0) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | |||
| 99 | 22 ( 27.5) | 148 ( 26.6) | |||
| TNM_PATH_T (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 0 ( 0.0) | 8 ( 1.4) | |||
| p1 | 0 ( 0.0) | 12 ( 2.2) | |||
| p1A | 0 ( 0.0) | 17 ( 3.1) | |||
| p1B | 0 ( 0.0) | 10 ( 1.8) | |||
| p1C | 0 ( 0.0) | 23 ( 4.1) | |||
| p1MI | 0 ( 0.0) | 7 ( 1.3) | |||
| p2 | 0 ( 0.0) | 12 ( 2.2) | |||
| p2A | 0 ( 0.0) | 0 ( 0.0) | |||
| p2B | 0 ( 0.0) | 0 ( 0.0) | |||
| p2C | 0 ( 0.0) | 0 ( 0.0) | |||
| p2D | 0 ( 0.0) | 0 ( 0.0) | |||
| p3 | 0 ( 0.0) | 2 ( 0.4) | |||
| p3A | 0 ( 0.0) | 0 ( 0.0) | |||
| p3B | 0 ( 0.0) | 0 ( 0.0) | |||
| p4 | 1 ( 1.2) | 0 ( 0.0) | |||
| p4A | 0 ( 0.0) | 0 ( 0.0) | |||
| p4B | 0 ( 0.0) | 5 ( 0.9) | |||
| p4C | 0 ( 0.0) | 0 ( 0.0) | |||
| p4D | 1 ( 1.2) | 2 ( 0.4) | |||
| pA | 0 ( 0.0) | 0 ( 0.0) | |||
| pIS | 5 ( 6.2) | 238 ( 42.7) | |||
| pX | 44 ( 55.0) | 196 ( 35.2) | |||
| NA | 29 ( 36.2) | 25 ( 4.5) | |||
| TNM_PATH_N (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 2 ( 2.5) | 216 ( 38.8) | |||
| p0I- | 0 ( 0.0) | 29 ( 5.2) | |||
| p0I+ | 0 ( 0.0) | 2 ( 0.4) | |||
| p0M- | 0 ( 0.0) | 0 ( 0.0) | |||
| p0M+ | 0 ( 0.0) | 0 ( 0.0) | |||
| p1 | 0 ( 0.0) | 7 ( 1.3) | |||
| p1A | 1 ( 1.2) | 8 ( 1.4) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | |||
| p1C | 0 ( 0.0) | 0 ( 0.0) | |||
| p1MI | 0 ( 0.0) | 2 ( 0.4) | |||
| p2 | 0 ( 0.0) | 2 ( 0.4) | |||
| p2A | 0 ( 0.0) | 2 ( 0.4) | |||
| p2B | 0 ( 0.0) | 0 ( 0.0) | |||
| p2C | 0 ( 0.0) | 0 ( 0.0) | |||
| p3 | 0 ( 0.0) | 2 ( 0.4) | |||
| p3A | 0 ( 0.0) | 3 ( 0.5) | |||
| p3B | 0 ( 0.0) | 0 ( 0.0) | |||
| p3C | 0 ( 0.0) | 0 ( 0.0) | |||
| p4 | 0 ( 0.0) | 0 ( 0.0) | |||
| pX | 47 ( 58.8) | 239 ( 42.9) | |||
| NA | 30 ( 37.5) | 45 ( 8.1) | |||
| TNM_PATH_M (%) | N_A | 0 ( 0.0) | 0 ( 0.0) | NaN | |
| p0 | 0 ( 0.0) | 0 ( 0.0) | |||
| p1 | 2 ( 2.5) | 6 ( 1.1) | |||
| p1A | 0 ( 0.0) | 0 ( 0.0) | |||
| p1B | 0 ( 0.0) | 0 ( 0.0) | |||
| p1C | 0 ( 0.0) | 0 ( 0.0) | |||
| pX | 32 ( 40.0) | 290 ( 52.1) | |||
| NA | 46 ( 57.5) | 261 ( 46.9) | |||
| TNM_PATH_STAGE_GROUP (%) | 0 | 2 ( 2.5) | 279 ( 50.1) | NaN | |
| 1 | 0 ( 0.0) | 51 ( 9.2) | |||
| 1A | 0 ( 0.0) | 26 ( 4.7) | |||
| 1B | 0 ( 0.0) | 2 ( 0.4) | |||
| 1C | 0 ( 0.0) | 0 ( 0.0) | |||
| 2 | 0 ( 0.0) | 3 ( 0.5) | |||
| 2A | 0 ( 0.0) | 21 ( 3.8) | |||
| 2B | 0 ( 0.0) | 8 ( 1.4) | |||
| 2C | 0 ( 0.0) | 0 ( 0.0) | |||
| 3 | 0 ( 0.0) | 0 ( 0.0) | |||
| 3A | 0 ( 0.0) | 4 ( 0.7) | |||
| 3B | 0 ( 0.0) | 6 ( 1.1) | |||
| 3C | 1 ( 1.2) | 4 ( 0.7) | |||
| 4 | 2 ( 2.5) | 7 ( 1.3) | |||
| 4A | 0 ( 0.0) | 0 ( 0.0) | |||
| 4A1 | 0 ( 0.0) | 0 ( 0.0) | |||
| 4B | 0 ( 0.0) | 0 ( 0.0) | |||
| 4C | 0 ( 0.0) | 0 ( 0.0) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | |||
| 99 | 55 ( 68.8) | 129 ( 23.2) | |||
| NA | 20 ( 25.0) | 17 ( 3.1) | |||
| DX_RX_STARTED_DAYS (mean (sd)) | 67.82 (96.52) | 34.85 (30.65) | <0.001 | ||
| DX_SURG_STARTED_DAYS (mean (sd)) | NaN (NA) | 39.70 (40.59) | NA | ||
| DX_DEFSURG_STARTED_DAYS (mean (sd)) | NaN (NA) | 47.18 (46.91) | NA | ||
| MARGINS (%) | No Residual | 0 ( 0.0) | 484 ( 86.9) | NaN | |
| Residual, NOS | 0 ( 0.0) | 10 ( 1.8) | |||
| Microscopic Resid | 0 ( 0.0) | 11 ( 2.0) | |||
| Macroscopic Resid | 0 ( 0.0) | 2 ( 0.4) | |||
| Not evaluable | 0 ( 0.0) | 0 ( 0.0) | |||
| No surg | 80 (100.0) | 33 ( 5.9) | |||
| Unknown | 0 ( 0.0) | 17 ( 3.1) | |||
| MARGINS_YN (%) | No | 0 ( 0.0) | 484 ( 86.9) | <0.001 | |
| Yes | 0 ( 0.0) | 23 ( 4.1) | |||
| No surg/Unk/NA | 80 (100.0) | 50 ( 9.0) | |||
| SURG_DISCHARGE_DAYS (mean (sd)) | NaN (NA) | 1.04 (4.16) | NA | ||
| READM_HOSP_30_DAYS_F (%) | No_Surg_or_No_Readmit | 77 ( 96.2) | 520 ( 93.4) | NaN | |
| Unplan_Readmit_Same | 0 ( 0.0) | 13 ( 2.3) | |||
| Plan_Readmit_Same | 0 ( 0.0) | 15 ( 2.7) | |||
| PlanUnplan_Same | 0 ( 0.0) | 0 ( 0.0) | |||
| 9 | 3 ( 3.8) | 9 ( 1.6) | |||
| RX_SUMM_RADIATION_F (%) | None | 80 (100.0) | 363 ( 65.2) | NaN | |
| Beam Radiation | 0 ( 0.0) | 191 ( 34.3) | |||
| Radioactive Implants | 0 ( 0.0) | 2 ( 0.4) | |||
| Radioisotopes | 0 ( 0.0) | 0 ( 0.0) | |||
| Beam + Imp or Isotopes | 0 ( 0.0) | 0 ( 0.0) | |||
| Radiation, NOS | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 0 ( 0.0) | 1 ( 0.2) | |||
| PUF_30_DAY_MORT_CD_F (%) | Alive_30 | 0 ( 0.0) | 515 ( 92.5) | <0.001 | |
| Dead_30 | 0 ( 0.0) | 1 ( 0.2) | |||
| Unknown | 0 ( 0.0) | 5 ( 0.9) | |||
| NA | 80 (100.0) | 36 ( 6.5) | |||
| PUF_90_DAY_MORT_CD_F (%) | Alive_90 | 0 ( 0.0) | 509 ( 91.4) | <0.001 | |
| Dead_90 | 0 ( 0.0) | 3 ( 0.5) | |||
| Unknown | 0 ( 0.0) | 9 ( 1.6) | |||
| NA | 80 (100.0) | 36 ( 6.5) | |||
| DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) | 28.26 (29.96) | 63.70 (42.43) | <0.001 | ||
| LYMPH_VASCULAR_INVASION_F (%) | Neg_LymphVasc_Inv | 9 ( 11.2) | 147 ( 26.4) | NaN | |
| Pos_LumphVasc_Inv | 0 ( 0.0) | 10 ( 1.8) | |||
| N_A | 0 ( 0.0) | 0 ( 0.0) | |||
| Unknown | 38 ( 47.5) | 106 ( 19.0) | |||
| NA | 33 ( 41.2) | 294 ( 52.8) | |||
| RX_HOSP_SURG_APPR_2010_F (%) | No_Surg | 47 ( 58.8) | 43 ( 7.7) | NaN | |
| Robot_Assist | 0 ( 0.0) | 0 ( 0.0) | |||
| Robot_to_Open | 0 ( 0.0) | 0 ( 0.0) | |||
| Endo_Lap | 0 ( 0.0) | 1 ( 0.2) | |||
| Endo_Lap_to_Open | 0 ( 0.0) | 0 ( 0.0) | |||
| Open_Unknown | 0 ( 0.0) | 219 ( 39.3) | |||
| Unknown | 0 ( 0.0) | 0 ( 0.0) | |||
| NA | 33 ( 41.2) | 294 ( 52.8) | |||
| SURG_RAD_SEQ (%) | Surg Alone | 0 ( 0.0) | 346 ( 62.1) | NaN | |
| Surg then Rad | 0 ( 0.0) | 174 ( 31.2) | |||
| Rad Alone | 0 ( 0.0) | 17 ( 3.1) | |||
| No Treatment | 80 (100.0) | 10 ( 1.8) | |||
| Other | 0 ( 0.0) | 8 ( 1.4) | |||
| Rad before and after Surg | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad then Surg | 0 ( 0.0) | 2 ( 0.4) | |||
| SURG_RAD_SEQ_C (%) | Surg, No rad, No Chemo | 0 ( 0.0) | 321 ( 57.6) | NaN | |
| Surg then Rad, No Chemo | 0 ( 0.0) | 141 ( 25.3) | |||
| Surg then Rad, Yes Chemo | 0 ( 0.0) | 33 ( 5.9) | |||
| Surg, No rad, Yes Chemo | 0 ( 0.0) | 25 ( 4.5) | |||
| No Surg, No Rad, Yes Chemo | 0 ( 0.0) | 9 ( 1.6) | |||
| No Surg, No Rad, No Chemo | 80 (100.0) | 1 ( 0.2) | |||
| Other | 0 ( 0.0) | 8 ( 1.4) | |||
| Rad, No Surg, Yes Chemo | 0 ( 0.0) | 5 ( 0.9) | |||
| Rad, No Surg, No Chemo | 0 ( 0.0) | 12 ( 2.2) | |||
| Rad then Surg, Yes Chemo | 0 ( 0.0) | 2 ( 0.4) | |||
| Rad then Surg, No Chemo | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad before and after Surg, Yes Chemo | 0 ( 0.0) | 0 ( 0.0) | |||
| Rad before and after Surg, No Chemo | 0 ( 0.0) | 0 ( 0.0) | |||
| T_SIZE (%) | No Tumor | 2 ( 2.5) | 2 ( 0.4) | 0.050 | |
| Microscopic focus | 0 ( 0.0) | 17 ( 3.1) | |||
| < 1 cm | 10 ( 12.5) | 68 ( 12.2) | |||
| 1-2 cm | 4 ( 5.0) | 82 ( 14.7) | |||
| 2-3 cm | 5 ( 6.2) | 47 ( 8.4) | |||
| 3-4 cm | 5 ( 6.2) | 22 ( 3.9) | |||
| 4-5 cm | 2 ( 2.5) | 8 ( 1.4) | |||
| 5-6 cm | 1 ( 1.2) | 7 ( 1.3) | |||
| >6 cm | 6 ( 7.5) | 21 ( 3.8) | |||
| NA_unk | 45 ( 56.2) | 283 ( 50.8) | |||
| SURGERY_YN (%) | No | 80 (100.0) | 27 ( 4.8) | <0.001 | |
| Ukn | 0 ( 0.0) | 7 ( 1.3) | |||
| Yes | 0 ( 0.0) | 523 ( 93.9) | |||
| RADIATION_YN (%) | No | 80 (100.0) | 363 ( 65.2) | <0.001 | |
| Yes | 0 ( 0.0) | 193 ( 34.6) | |||
| NA | 0 ( 0.0) | 1 ( 0.2) | |||
| CHEMO_YN (%) | No | 80 (100.0) | 483 ( 86.7) | NaN | |
| Yes | 0 ( 0.0) | 74 ( 13.3) | |||
| Ukn | 0 ( 0.0) | 0 ( 0.0) | |||
| mets_at_dx (%) | Bone | 3 ( 3.8) | 7 ( 1.3) | NaN | |
| Brain | 0 ( 0.0) | 0 ( 0.0) | |||
| Liver | 0 ( 0.0) | 1 ( 0.2) | |||
| Lung | 1 ( 1.2) | 2 ( 0.4) | |||
| None/Other/Unk/NA | 76 ( 95.0) | 547 ( 98.2) | |||
| IMMUNO_YN (%) | No | 80 (100.0) | 550 ( 98.7) | 0.602 | |
| Yes | 0 ( 0.0) | 5 ( 0.9) | |||
| Ukn | 0 ( 0.0) | 2 ( 0.4) | |||
| Tx_YN (%) | FALSE | 80 (100.0) | 0 ( 0.0) | NaN | |
| TRUE | 0 ( 0.0) | 557 (100.0) | |||
| NA | 0 ( 0.0) | 0 ( 0.0) | |||
| MEDICAID_EXPN_CODE (%) | Non-Expansion State | 31 ( 38.8) | 220 ( 39.5) | 0.566 | |
| Jan 2014 Expansion States | 28 ( 35.0) | 170 ( 30.5) | |||
| Early Expansion States (2010-13) | 9 ( 11.2) | 59 ( 10.6) | |||
| Late Expansion States (> Jan 2014) | 11 ( 13.8) | 78 ( 14.0) | |||
| Suppressed for Ages 0 - 39 | 1 ( 1.2) | 30 ( 5.4) |
Kaplan Meier Analysis
All
uni_var(test_var = "All", data_imp = data)_________________________________________________
## All
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ All, data = data)
n events median 0.95LCL 0.95UCL
675 180 138 133 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ All, data = data)
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 564 46 0.929 0.0102 0.909 0.949
24 497 23 0.889 0.0127 0.864 0.914
36 427 25 0.842 0.0151 0.813 0.872
48 359 19 0.802 0.0169 0.770 0.836
60 310 13 0.772 0.0183 0.737 0.808
120 79 46 0.596 0.0282 0.543 0.653
## Univariable Cox Proportional Hazard Model for: All
[1] "Only one level, no Cox model performed"
## Unadjusted Kaplan Meier Overall Survival Curve for: All
Facility Type
uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)_________________________________________________
## FACILITY_TYPE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_TYPE_F, data = data)
35 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
FACILITY_TYPE_F=Community Cancer Program 81 26 138 108 NA
FACILITY_TYPE_F=Comprehensive Comm Ca Program 296 83 134 108 NA
FACILITY_TYPE_F=Academic/Research Program 173 44 NA 137 NA
FACILITY_TYPE_F=Integrated Network Ca Program 90 26 133 128 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_TYPE_F, data = data)
35 observations deleted due to missingness
FACILITY_TYPE_F=Community Cancer Program
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 66 7 0.909 0.0328 0.847 0.976
24 60 4 0.853 0.0411 0.776 0.937
36 51 2 0.822 0.0449 0.739 0.915
48 45 6 0.726 0.0543 0.627 0.840
60 39 1 0.708 0.0558 0.607 0.826
120 11 5 0.573 0.0722 0.448 0.734
FACILITY_TYPE_F=Comprehensive Comm Ca Program
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 244 23 0.918 0.0164 0.886 0.951
24 210 9 0.882 0.0196 0.844 0.921
36 172 13 0.824 0.0240 0.779 0.873
48 141 6 0.794 0.0262 0.744 0.847
60 121 6 0.758 0.0289 0.703 0.816
120 30 23 0.540 0.0469 0.456 0.640
FACILITY_TYPE_F=Academic/Research Program
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 146 9 0.946 0.0175 0.912 0.981
24 128 7 0.899 0.0241 0.853 0.947
36 118 5 0.862 0.0281 0.809 0.919
48 99 5 0.824 0.0317 0.764 0.888
60 87 3 0.798 0.0340 0.734 0.868
120 24 14 0.605 0.0533 0.509 0.720
FACILITY_TYPE_F=Integrated Network Ca Program
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 76 7 0.919 0.0294 0.863 0.978
24 70 3 0.882 0.0352 0.815 0.953
36 59 4 0.828 0.0420 0.750 0.915
48 51 2 0.799 0.0454 0.714 0.893
60 43 3 0.749 0.0509 0.656 0.856
120 10 4 0.648 0.0653 0.532 0.790
## Univariable Cox Proportional Hazard Model for: FACILITY_TYPE_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_TYPE_F, data = data)
n= 640, number of events= 179
(35 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
FACILITY_TYPE_FComprehensive Comm Ca Program 0.001391 1.001392 0.225451 0.006 0.995
FACILITY_TYPE_FAcademic/Research Program -0.223626 0.799614 0.247849 -0.902 0.367
FACILITY_TYPE_FIntegrated Network Ca Program -0.085972 0.917620 0.277593 -0.310 0.757
exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program 1.0014 0.9986 0.6437 1.558
FACILITY_TYPE_FAcademic/Research Program 0.7996 1.2506 0.4919 1.300
FACILITY_TYPE_FIntegrated Network Ca Program 0.9176 1.0898 0.5326 1.581
Concordance= 0.525 (se = 0.022 )
Rsquare= 0.003 (max possible= 0.961 )
Likelihood ratio test= 1.63 on 3 df, p=0.6515
Wald test = 1.59 on 3 df, p=0.6614
Score (logrank) test = 1.6 on 3 df, p=0.66Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: FACILITY_TYPE_F
Facility Location
uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)_________________________________________________
## FACILITY_LOCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_LOCATION_F, data = data)
35 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
FACILITY_LOCATION_F=New England 31 7 NA 88.7 NA
FACILITY_LOCATION_F=Middle Atlantic 93 23 138 108.0 NA
FACILITY_LOCATION_F=South Atlantic 143 36 137 133.0 NA
FACILITY_LOCATION_F=East North Central 134 37 137 127.7 NA
FACILITY_LOCATION_F=East South Central 44 18 110 73.2 NA
FACILITY_LOCATION_F=West North Central 59 22 102 83.6 NA
FACILITY_LOCATION_F=West South Central 60 21 115 67.6 NA
FACILITY_LOCATION_F=Mountain 35 6 NA NA NA
FACILITY_LOCATION_F=Pacific 41 9 NA 94.5 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_LOCATION_F, data = data)
35 observations deleted due to missingness
FACILITY_LOCATION_F=New England
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 23 3 0.895 0.0575 0.790 1.00
24 21 0 0.895 0.0575 0.790 1.00
36 16 1 0.843 0.0744 0.709 1.00
48 13 2 0.737 0.0954 0.572 0.95
60 12 0 0.737 0.0954 0.572 0.95
120 5 1 0.664 0.1107 0.479 0.92
FACILITY_LOCATION_F=Middle Atlantic
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 77 6 0.933 0.0266 0.882 0.986
24 67 3 0.895 0.0331 0.833 0.963
36 62 0 0.895 0.0331 0.833 0.963
48 51 5 0.820 0.0442 0.738 0.912
60 46 1 0.804 0.0464 0.718 0.900
120 9 7 0.597 0.0794 0.460 0.775
FACILITY_LOCATION_F=South Atlantic
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 123 5 0.963 0.0160 0.933 0.995
24 104 7 0.906 0.0260 0.856 0.958
36 87 4 0.869 0.0308 0.810 0.931
48 75 4 0.827 0.0358 0.759 0.900
60 64 4 0.781 0.0406 0.705 0.864
120 19 8 0.624 0.0609 0.515 0.755
FACILITY_LOCATION_F=East North Central
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 114 8 0.938 0.0212 0.897 0.981
24 103 5 0.896 0.0274 0.844 0.951
36 93 5 0.851 0.0325 0.790 0.917
48 76 6 0.792 0.0382 0.721 0.871
60 64 3 0.760 0.0410 0.683 0.844
120 17 7 0.646 0.0538 0.549 0.761
FACILITY_LOCATION_F=East South Central
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 35 7 0.834 0.0572 0.729 0.954
24 34 1 0.810 0.0604 0.700 0.938
36 30 4 0.715 0.0696 0.591 0.865
48 29 0 0.715 0.0696 0.591 0.865
60 25 0 0.715 0.0696 0.591 0.865
120 2 6 0.452 0.1102 0.280 0.729
FACILITY_LOCATION_F=West North Central
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 52 4 0.930 0.0339 0.866 0.999
24 44 5 0.838 0.0496 0.746 0.941
36 39 3 0.779 0.0566 0.675 0.898
48 33 2 0.739 0.0604 0.629 0.867
60 28 2 0.690 0.0654 0.573 0.831
120 5 6 0.430 0.0973 0.276 0.670
FACILITY_LOCATION_F=West South Central
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 46 7 0.876 0.0439 0.794 0.966
24 39 1 0.857 0.0469 0.770 0.954
36 32 3 0.791 0.0567 0.687 0.910
48 28 0 0.791 0.0567 0.687 0.910
60 24 2 0.731 0.0665 0.612 0.874
120 5 8 0.396 0.1016 0.239 0.655
FACILITY_LOCATION_F=Mountain
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 28 3 0.911 0.0492 0.819 1.000
24 26 0 0.911 0.0492 0.819 1.000
36 22 2 0.838 0.0670 0.716 0.980
48 16 0 0.838 0.0670 0.716 0.980
60 15 0 0.838 0.0670 0.716 0.980
120 8 1 0.768 0.0908 0.609 0.968
FACILITY_LOCATION_F=Pacific
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 34 3 0.923 0.0429 0.842 1.000
24 30 1 0.893 0.0508 0.799 0.998
36 19 2 0.823 0.0670 0.701 0.965
48 15 0 0.823 0.0670 0.701 0.965
60 12 1 0.759 0.0867 0.607 0.950
120 5 2 0.621 0.1133 0.435 0.888
## Univariable Cox Proportional Hazard Model for: FACILITY_LOCATION_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_LOCATION_F, data = data)
n= 640, number of events= 179
(35 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
FACILITY_LOCATION_FMiddle Atlantic 0.07418 1.07700 0.43257 0.171 0.864
FACILITY_LOCATION_FSouth Atlantic 0.09900 1.10407 0.41366 0.239 0.811
FACILITY_LOCATION_FEast North Central 0.19621 1.21678 0.41332 0.475 0.635
FACILITY_LOCATION_FEast South Central 0.55115 1.73525 0.44683 1.233 0.217
FACILITY_LOCATION_FWest North Central 0.52098 1.68368 0.43483 1.198 0.231
FACILITY_LOCATION_FWest South Central 0.56765 1.76411 0.43685 1.299 0.194
FACILITY_LOCATION_FMountain -0.32226 0.72451 0.55642 -0.579 0.562
FACILITY_LOCATION_FPacific 0.11805 1.12530 0.50497 0.234 0.815
exp(coef) exp(-coef) lower .95 upper .95
FACILITY_LOCATION_FMiddle Atlantic 1.0770 0.9285 0.4613 2.514
FACILITY_LOCATION_FSouth Atlantic 1.1041 0.9057 0.4908 2.484
FACILITY_LOCATION_FEast North Central 1.2168 0.8218 0.5412 2.735
FACILITY_LOCATION_FEast South Central 1.7352 0.5763 0.7228 4.166
FACILITY_LOCATION_FWest North Central 1.6837 0.5939 0.7180 3.948
FACILITY_LOCATION_FWest South Central 1.7641 0.5669 0.7493 4.153
FACILITY_LOCATION_FMountain 0.7245 1.3802 0.2435 2.156
FACILITY_LOCATION_FPacific 1.1253 0.8887 0.4182 3.028
Concordance= 0.557 (se = 0.023 )
Rsquare= 0.015 (max possible= 0.961 )
Likelihood ratio test= 9.48 on 8 df, p=0.3035
Wald test = 9.52 on 8 df, p=0.3002
Score (logrank) test = 9.75 on 8 df, p=0.283Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: FACILITY_LOCATION_F
Facility Geography
uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)_________________________________________________
## FACILITY_GEOGRAPHY
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_GEOGRAPHY, data = data)
35 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
FACILITY_GEOGRAPHY=Northeast 124 30 NA 138 NA
FACILITY_GEOGRAPHY=South 203 57 134 115 NA
FACILITY_GEOGRAPHY=Midwest 237 77 133 108 NA
FACILITY_GEOGRAPHY=West 76 15 NA NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_GEOGRAPHY, data = data)
35 observations deleted due to missingness
FACILITY_GEOGRAPHY=Northeast
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 100 9 0.924 0.0244 0.877 0.973
24 88 3 0.895 0.0287 0.841 0.953
36 78 1 0.884 0.0305 0.826 0.946
48 64 7 0.802 0.0404 0.727 0.886
60 58 1 0.789 0.0417 0.712 0.876
120 14 8 0.621 0.0640 0.508 0.760
FACILITY_GEOGRAPHY=South
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 169 12 0.938 0.0174 0.904 0.972
24 143 8 0.891 0.0232 0.847 0.937
36 119 7 0.846 0.0276 0.793 0.901
48 103 4 0.815 0.0305 0.758 0.877
60 88 6 0.765 0.0348 0.700 0.837
120 24 16 0.553 0.0548 0.455 0.671
FACILITY_GEOGRAPHY=Midwest
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 201 19 0.916 0.0184 0.881 0.953
24 181 11 0.865 0.0230 0.821 0.911
36 162 12 0.806 0.0269 0.755 0.861
48 138 8 0.765 0.0293 0.709 0.824
60 117 5 0.735 0.0311 0.676 0.798
120 24 19 0.548 0.0466 0.464 0.647
FACILITY_GEOGRAPHY=West
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 62 6 0.917 0.0324 0.856 0.983
24 56 1 0.901 0.0356 0.834 0.974
36 41 4 0.831 0.0471 0.744 0.929
48 31 0 0.831 0.0471 0.744 0.929
60 27 1 0.801 0.0540 0.702 0.914
120 13 3 0.695 0.0739 0.564 0.856
## Univariable Cox Proportional Hazard Model for: FACILITY_GEOGRAPHY
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_GEOGRAPHY, data = data)
n= 640, number of events= 179
(35 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
FACILITY_GEOGRAPHYSouth 0.1907 1.2101 0.2256 0.845 0.398
FACILITY_GEOGRAPHYMidwest 0.3003 1.3502 0.2156 1.393 0.164
FACILITY_GEOGRAPHYWest -0.1390 0.8702 0.3165 -0.439 0.661
exp(coef) exp(-coef) lower .95 upper .95
FACILITY_GEOGRAPHYSouth 1.2101 0.8264 0.7776 1.883
FACILITY_GEOGRAPHYMidwest 1.3502 0.7406 0.8849 2.060
FACILITY_GEOGRAPHYWest 0.8702 1.1491 0.4680 1.618
Concordance= 0.532 (se = 0.023 )
Rsquare= 0.006 (max possible= 0.961 )
Likelihood ratio test= 3.77 on 3 df, p=0.2874
Wald test = 3.6 on 3 df, p=0.3076
Score (logrank) test = 3.64 on 3 df, p=0.3033Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: FACILITY_GEOGRAPHY
Age Group
uni_var(test_var = "AGE_F", data_imp = data)_________________________________________________
## AGE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_F, data = data)
n events median 0.95LCL 0.95UCL
AGE_F=(0,54] 161 17 NA NA NA
AGE_F=(54,64] 149 28 137.0 134 NA
AGE_F=(64,74] 149 30 153.1 115 NA
AGE_F=(74,100] 216 105 73.2 60 93.4
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_F, data = data)
AGE_F=(0,54]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 145 3 0.981 0.0109 0.960 1.000
24 129 4 0.952 0.0176 0.918 0.988
36 116 5 0.914 0.0239 0.868 0.962
48 104 1 0.905 0.0251 0.857 0.956
60 96 0 0.905 0.0251 0.857 0.956
120 29 4 0.858 0.0334 0.795 0.926
AGE_F=(54,64]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 121 11 0.921 0.0227 0.878 0.967
24 108 3 0.898 0.0259 0.849 0.950
36 94 2 0.881 0.0282 0.827 0.938
48 75 4 0.840 0.0334 0.777 0.908
60 68 1 0.829 0.0347 0.764 0.900
120 20 4 0.737 0.0552 0.636 0.853
AGE_F=(64,74]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 131 5 0.965 0.0152 0.936 0.996
24 116 6 0.919 0.0235 0.874 0.966
36 100 3 0.894 0.0268 0.843 0.948
48 84 3 0.866 0.0306 0.808 0.928
60 71 0 0.866 0.0306 0.808 0.928
120 17 12 0.609 0.0719 0.483 0.767
AGE_F=(74,100]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 167 27 0.867 0.0238 0.822 0.915
24 144 10 0.813 0.0279 0.760 0.869
36 117 15 0.724 0.0329 0.662 0.792
48 96 11 0.653 0.0361 0.586 0.727
60 75 12 0.565 0.0391 0.494 0.647
120 13 26 0.301 0.0446 0.225 0.402
## Univariable Cox Proportional Hazard Model for: AGE_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
AGE_F(54,64] 0.7831 2.1883 0.3086 2.537 0.0112 *
AGE_F(64,74] 0.8140 2.2570 0.3039 2.678 0.0074 **
AGE_F(74,100] 1.9331 6.9109 0.2636 7.334 2.24e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
AGE_F(54,64] 2.188 0.4570 1.195 4.007
AGE_F(64,74] 2.257 0.4431 1.244 4.095
AGE_F(74,100] 6.911 0.1447 4.123 11.585
Concordance= 0.666 (se = 0.023 )
Rsquare= 0.126 (max possible= 0.956 )
Likelihood ratio test= 90.86 on 3 df, p=0
Wald test = 82.35 on 3 df, p=0
Score (logrank) test = 99.33 on 3 df, p=0Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: AGE_F
Age Group
uni_var(test_var = "AGE_40", data_imp = data)_________________________________________________
## AGE_40
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_40, data = data)
n events median 0.95LCL 0.95UCL
AGE_40=(0,40] 38 1 NA NA NA
AGE_40=(40,100] 637 179 137 133 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_40, data = data)
AGE_40=(0,40]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 35 0 1.000 0.0000 1.000 1
24 32 0 1.000 0.0000 1.000 1
36 29 1 0.967 0.0328 0.905 1
48 25 0 0.967 0.0328 0.905 1
60 22 0 0.967 0.0328 0.905 1
120 4 0 0.967 0.0328 0.905 1
AGE_40=(40,100]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 529 46 0.924 0.0108 0.903 0.946
24 465 23 0.882 0.0134 0.856 0.909
36 398 24 0.834 0.0158 0.804 0.866
48 334 19 0.792 0.0177 0.758 0.828
60 288 13 0.760 0.0192 0.723 0.798
120 75 46 0.577 0.0289 0.523 0.637
## Univariable Cox Proportional Hazard Model for: AGE_40
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_40, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
AGE_40(40,100] 2.460 11.708 1.003 2.453 0.0142 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
AGE_40(40,100] 11.71 0.08541 1.64 83.58
Concordance= 0.528 (se = 0.01 )
Rsquare= 0.023 (max possible= 0.956 )
Likelihood ratio test= 15.87 on 1 df, p=6.772e-05
Wald test = 6.02 on 1 df, p=0.01416
Score (logrank) test = 9.74 on 1 df, p=0.001805Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: AGE_40
Gender
uni_var(test_var = "SEX_F", data_imp = data)_________________________________________________
## SEX_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SEX_F, data = data)
n events median 0.95LCL 0.95UCL
SEX_F=Male 19 4 NA 101 NA
SEX_F=Female 656 176 153 133 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SEX_F, data = data)
SEX_F=Male
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 16 1 0.941 0.0571 0.836 1
24 14 0 0.941 0.0571 0.836 1
36 13 1 0.874 0.0837 0.724 1
48 11 0 0.874 0.0837 0.724 1
60 10 0 0.874 0.0837 0.724 1
120 3 2 0.546 0.1938 0.273 1
SEX_F=Female
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 548 45 0.928 0.0103 0.908 0.949
24 483 23 0.887 0.0129 0.862 0.913
36 414 24 0.841 0.0153 0.812 0.872
48 348 19 0.800 0.0172 0.767 0.835
60 300 13 0.769 0.0186 0.733 0.806
120 76 44 0.598 0.0283 0.545 0.656
## Univariable Cox Proportional Hazard Model for: SEX_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SEX_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
SEX_FFemale 0.2633 1.3012 0.5059 0.52 0.603
exp(coef) exp(-coef) lower .95 upper .95
SEX_FFemale 1.301 0.7685 0.4827 3.507
Concordance= 0.506 (se = 0.007 )
Rsquare= 0 (max possible= 0.956 )
Likelihood ratio test= 0.29 on 1 df, p=0.5872
Wald test = 0.27 on 1 df, p=0.6028
Score (logrank) test = 0.27 on 1 df, p=0.6018Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: SEX_F
RACE_F
uni_var(test_var = "RACE_F", data_imp = data)_________________________________________________
## RACE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RACE_F, data = data)
n events median 0.95LCL 0.95UCL
RACE_F=White 570 151 138.2 133.0 NA
RACE_F=Black 77 26 93.4 78.1 NA
RACE_F=Other/Unk 16 2 NA NA NA
RACE_F=Asian 12 1 NA NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RACE_F, data = data)
RACE_F=White
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 479 36 0.934 0.0107 0.913 0.955
24 424 19 0.895 0.0134 0.869 0.922
36 364 21 0.849 0.0161 0.818 0.881
48 301 18 0.804 0.0184 0.769 0.841
60 256 12 0.770 0.0201 0.732 0.810
120 70 37 0.601 0.0305 0.544 0.664
RACE_F=Black
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 62 7 0.904 0.0344 0.839 0.974
24 52 4 0.843 0.0437 0.761 0.933
36 45 4 0.776 0.0515 0.681 0.884
48 41 1 0.758 0.0534 0.660 0.870
60 38 1 0.739 0.0552 0.639 0.856
120 3 9 0.412 0.0975 0.259 0.655
RACE_F=Other/Unk
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 13 2 0.867 0.0878 0.711 1
24 13 0 0.867 0.0878 0.711 1
36 11 0 0.867 0.0878 0.711 1
48 11 0 0.867 0.0878 0.711 1
60 11 0 0.867 0.0878 0.711 1
120 5 0 0.867 0.0878 0.711 1
RACE_F=Asian
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 10 1 0.909 0.0867 0.754 1
24 8 0 0.909 0.0867 0.754 1
36 7 0 0.909 0.0867 0.754 1
48 6 0 0.909 0.0867 0.754 1
60 5 0 0.909 0.0867 0.754 1
120 1 0 0.909 0.0867 0.754 1
## Univariable Cox Proportional Hazard Model for: RACE_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RACE_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
RACE_FBlack 0.4011 1.4935 0.2132 1.881 0.0599 .
RACE_FOther/Unk -1.1059 0.3309 0.7128 -1.551 0.1208
RACE_FAsian -0.9551 0.3848 1.0036 -0.952 0.3412
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
RACE_FBlack 1.4935 0.6696 0.98336 2.268
RACE_FOther/Unk 0.3309 3.0221 0.08183 1.338
RACE_FAsian 0.3848 2.5990 0.05382 2.751
Concordance= 0.529 (se = 0.015 )
Rsquare= 0.013 (max possible= 0.956 )
Likelihood ratio test= 8.51 on 3 df, p=0.03653
Wald test = 7.2 on 3 df, p=0.06593
Score (logrank) test = 7.69 on 3 df, p=0.05295Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: RACE_F
Hispanic
uni_var(test_var = "HISPANIC", data_imp = data)_________________________________________________
## HISPANIC
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ HISPANIC, data = data)
n events median 0.95LCL 0.95UCL
HISPANIC=No 587 156 138 133 NA
HISPANIC=Yes 27 3 NA NA NA
HISPANIC=Unknown 61 21 NA 128 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ HISPANIC, data = data)
HISPANIC=No
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 485 40 0.928 0.0110 0.907 0.950
24 427 21 0.886 0.0138 0.860 0.914
36 366 22 0.838 0.0164 0.807 0.871
48 306 16 0.800 0.0183 0.765 0.836
60 261 12 0.767 0.0198 0.729 0.806
120 64 39 0.579 0.0318 0.520 0.644
HISPANIC=Yes
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 24 1 0.962 0.0377 0.890 1
24 20 0 0.962 0.0377 0.890 1
36 15 1 0.913 0.0590 0.805 1
48 10 1 0.830 0.0956 0.663 1
60 8 0 0.830 0.0956 0.663 1
120 1 0 0.830 0.0956 0.663 1
HISPANIC=Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 55 5 0.918 0.0352 0.851 0.989
24 50 2 0.883 0.0415 0.806 0.969
36 46 2 0.847 0.0471 0.759 0.944
48 43 2 0.810 0.0517 0.715 0.918
60 41 1 0.790 0.0541 0.691 0.904
120 14 7 0.643 0.0668 0.525 0.789
## Univariable Cox Proportional Hazard Model for: HISPANIC
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ HISPANIC, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
HISPANICYes -0.68962 0.50177 0.58325 -1.182 0.237
HISPANICUnknown -0.04505 0.95595 0.23390 -0.193 0.847
exp(coef) exp(-coef) lower .95 upper .95
HISPANICYes 0.5018 1.993 0.1600 1.574
HISPANICUnknown 0.9559 1.046 0.6044 1.512
Concordance= 0.507 (se = 0.014 )
Rsquare= 0.003 (max possible= 0.956 )
Likelihood ratio test= 1.78 on 2 df, p=0.4117
Wald test = 1.42 on 2 df, p=0.4923
Score (logrank) test = 1.47 on 2 df, p=0.4789Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: HISPANIC
Insurance Status
uni_var(test_var = "INSURANCE_F", data_imp = data)_________________________________________________
## INSURANCE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ INSURANCE_F, data = data)
n events median 0.95LCL 0.95UCL
INSURANCE_F=Private 273 35 NA NA NA
INSURANCE_F=None 24 11 78.1 34.2 NA
INSURANCE_F=Medicaid 35 9 NA 89.8 NA
INSURANCE_F=Medicare 320 120 96.6 80.5 133
INSURANCE_F=Other Government 8 1 NA 74.3 NA
INSURANCE_F=Unknown 15 4 NA 73.2 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ INSURANCE_F, data = data)
INSURANCE_F=Private
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 234 9 0.965 0.0114 0.943 0.988
24 209 6 0.940 0.0152 0.910 0.970
36 191 3 0.926 0.0170 0.893 0.959
48 164 4 0.906 0.0193 0.869 0.944
60 154 0 0.906 0.0193 0.869 0.944
120 48 10 0.804 0.0366 0.735 0.879
INSURANCE_F=None
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 20 3 0.870 0.0702 0.742 1.000
24 18 2 0.783 0.0860 0.631 0.971
36 13 3 0.638 0.1030 0.465 0.876
48 10 1 0.580 0.1087 0.402 0.838
60 9 0 0.580 0.1087 0.402 0.838
120 1 2 0.322 0.1563 0.125 0.834
INSURANCE_F=Medicaid
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 27 3 0.903 0.0533 0.804 1.000
24 24 1 0.867 0.0622 0.753 0.998
36 18 4 0.721 0.0844 0.573 0.907
48 15 0 0.721 0.0844 0.573 0.907
60 11 0 0.721 0.0844 0.573 0.907
120 5 1 0.641 0.1065 0.462 0.887
INSURANCE_F=Medicare
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 264 29 0.905 0.0167 0.873 0.939
24 229 14 0.855 0.0206 0.815 0.896
36 190 15 0.795 0.0242 0.749 0.844
48 155 14 0.733 0.0274 0.682 0.789
60 123 13 0.667 0.0305 0.610 0.730
120 25 30 0.429 0.0419 0.354 0.520
INSURANCE_F=Other Government
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 0 1 0 1 1
24 7 0 1 0 1 1
36 6 0 1 0 1 1
48 6 0 1 0 1 1
60 5 0 1 0 1 1
INSURANCE_F=Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 11 2 0.867 0.0878 0.711 1
24 10 0 0.867 0.0878 0.711 1
36 9 0 0.867 0.0878 0.711 1
48 9 0 0.867 0.0878 0.711 1
60 8 0 0.867 0.0878 0.711 1
## Univariable Cox Proportional Hazard Model for: INSURANCE_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ INSURANCE_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
INSURANCE_FNone 1.65037 5.20891 0.34718 4.754 2.0e-06 ***
INSURANCE_FMedicaid 0.91602 2.49931 0.37412 2.448 0.0143 *
INSURANCE_FMedicare 1.33160 3.78708 0.19299 6.900 5.2e-12 ***
INSURANCE_FOther Government 0.03798 1.03871 1.01476 0.037 0.9701
INSURANCE_FUnknown 1.09154 2.97886 0.52924 2.062 0.0392 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
INSURANCE_FNone 5.209 0.1920 2.6377 10.286
INSURANCE_FMedicaid 2.499 0.4001 1.2005 5.203
INSURANCE_FMedicare 3.787 0.2641 2.5944 5.528
INSURANCE_FOther Government 1.039 0.9627 0.1421 7.590
INSURANCE_FUnknown 2.979 0.3357 1.0557 8.405
Concordance= 0.642 (se = 0.021 )
Rsquare= 0.09 (max possible= 0.956 )
Likelihood ratio test= 63.7 on 5 df, p=2.086e-12
Wald test = 52.65 on 5 df, p=3.959e-10
Score (logrank) test = 60.62 on 5 df, p=9.044e-12Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: INSURANCE_F
Overall Survival pre/post-ACA expansion
uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)_________________________________________________
## EXPN_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EXPN_GROUP, data = no_Excludes)
n events median 0.95LCL 0.95UCL
EXPN_GROUP=Post-Expansion 63 10 NA 54.2 NA
EXPN_GROUP=Pre-Expansion 601 172 138 133.0 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EXPN_GROUP, data = no_Excludes)
EXPN_GROUP=Post-Expansion
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 48 6 0.898 0.0396 0.823 0.979
24 39 2 0.858 0.0468 0.771 0.955
36 21 0 0.858 0.0468 0.771 0.955
48 10 1 0.780 0.0857 0.629 0.968
60 6 1 0.683 0.1181 0.486 0.958
EXPN_GROUP=Pre-Expansion
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 505 41 0.929 0.0108 0.908 0.950
24 446 22 0.886 0.0135 0.860 0.913
36 396 24 0.837 0.0161 0.806 0.869
48 342 18 0.798 0.0178 0.764 0.833
60 299 12 0.768 0.0191 0.732 0.807
120 81 47 0.588 0.0284 0.535 0.646
## Univariable Cox Proportional Hazard Model for: EXPN_GROUP
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EXPN_GROUP, data = no_Excludes)
n= 664, number of events= 182
coef exp(coef) se(coef) z Pr(>|z|)
EXPN_GROUPPre-Expansion -0.08197 0.92130 0.33000 -0.248 0.804
exp(coef) exp(-coef) lower .95 upper .95
EXPN_GROUPPre-Expansion 0.9213 1.085 0.4825 1.759
Concordance= 0.504 (se = 0.011 )
Rsquare= 0 (max possible= 0.959 )
Likelihood ratio test= 0.06 on 1 df, p=0.806
Wald test = 0.06 on 1 df, p=0.8038
Score (logrank) test = 0.06 on 1 df, p=0.8038
## Unadjusted Kaplan Meier Overall Survival Curve for: EXPN_GROUP
Education
uni_var(test_var = "EDUCATION_F", data_imp = data)_________________________________________________
## EDUCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EDUCATION_F, data = data)
4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
EDUCATION_F=21% or more 99 32 107 73.2 NA
EDUCATION_F=13 - 20.9% 164 51 116 98.3 NA
EDUCATION_F=7 - 12.9% 222 57 NA 132.9 NA
EDUCATION_F=Less than 7% 186 40 153 136.8 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EDUCATION_F, data = data)
4 observations deleted due to missingness
EDUCATION_F=21% or more
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 79 9 0.905 0.0302 0.848 0.966
24 69 3 0.869 0.0355 0.802 0.941
36 53 8 0.763 0.0469 0.676 0.861
48 41 3 0.713 0.0520 0.618 0.823
60 35 2 0.674 0.0559 0.573 0.793
120 5 7 0.455 0.0839 0.317 0.653
EDUCATION_F=13 - 20.9%
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 134 13 0.916 0.0224 0.873 0.961
24 113 9 0.852 0.0293 0.796 0.911
36 100 4 0.820 0.0322 0.760 0.886
48 80 6 0.769 0.0364 0.701 0.843
60 66 3 0.739 0.0389 0.666 0.819
120 13 15 0.482 0.0623 0.374 0.620
EDUCATION_F=7 - 12.9%
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 191 15 0.930 0.0175 0.896 0.965
24 175 6 0.900 0.0208 0.860 0.942
36 150 8 0.856 0.0249 0.809 0.906
48 129 6 0.821 0.0277 0.769 0.877
60 113 5 0.787 0.0304 0.730 0.849
120 28 15 0.631 0.0457 0.547 0.727
EDUCATION_F=Less than 7%
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 157 9 0.949 0.0164 0.918 0.982
24 138 5 0.917 0.0212 0.877 0.960
36 123 5 0.883 0.0254 0.834 0.934
48 108 4 0.853 0.0287 0.798 0.911
60 95 3 0.828 0.0311 0.770 0.892
120 33 9 0.716 0.0456 0.632 0.811
## Univariable Cox Proportional Hazard Model for: EDUCATION_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EDUCATION_F, data = data)
n= 671, number of events= 180
(4 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
EDUCATION_F13 - 20.9% -0.1679 0.8454 0.2259 -0.743 0.45730
EDUCATION_F7 - 12.9% -0.4740 0.6225 0.2215 -2.140 0.03239 *
EDUCATION_FLess than 7% -0.7328 0.4806 0.2389 -3.068 0.00216 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
EDUCATION_F13 - 20.9% 0.8454 1.183 0.5430 1.3163
EDUCATION_F7 - 12.9% 0.6225 1.606 0.4033 0.9610
EDUCATION_FLess than 7% 0.4806 2.081 0.3009 0.7675
Concordance= 0.572 (se = 0.023 )
Rsquare= 0.018 (max possible= 0.956 )
Likelihood ratio test= 12.16 on 3 df, p=0.006867
Wald test = 12.15 on 3 df, p=0.006897
Score (logrank) test = 12.46 on 3 df, p=0.005956Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: EDUCATION_F
Urban/Rural
uni_var(test_var = "U_R_F", data_imp = data)_________________________________________________
## U_R_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ U_R_F, data = data)
18 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
U_R_F=Metro 557 151 137 133 NA
U_R_F=Urban 88 24 NA 110 NA
U_R_F=Rural 12 3 NA 47 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ U_R_F, data = data)
18 observations deleted due to missingness
U_R_F=Metro
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 462 42 0.921 0.0117 0.899 0.944
24 407 19 0.882 0.0143 0.854 0.910
36 350 19 0.839 0.0167 0.807 0.872
48 300 12 0.808 0.0182 0.773 0.845
60 262 12 0.775 0.0199 0.737 0.815
120 63 39 0.595 0.0310 0.538 0.659
U_R_F=Urban
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 78 3 0.964 0.0203 0.925 1.000
24 71 3 0.926 0.0291 0.871 0.985
36 61 5 0.858 0.0398 0.783 0.940
48 45 6 0.768 0.0499 0.676 0.872
60 37 1 0.748 0.0523 0.652 0.858
120 13 6 0.593 0.0716 0.468 0.751
U_R_F=Rural
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 9 1 0.900 0.0949 0.732 1
24 8 0 0.900 0.0949 0.732 1
36 6 1 0.771 0.1442 0.535 1
48 4 1 0.617 0.1798 0.349 1
60 4 0 0.617 0.1798 0.349 1
120 1 0 0.617 0.1798 0.349 1
## Univariable Cox Proportional Hazard Model for: U_R_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ U_R_F, data = data)
n= 657, number of events= 178
(18 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
U_R_FUrban -0.06257 0.93934 0.21994 -0.284 0.776
U_R_FRural 0.14133 1.15181 0.58359 0.242 0.809
exp(coef) exp(-coef) lower .95 upper .95
U_R_FUrban 0.9393 1.0646 0.6104 1.446
U_R_FRural 1.1518 0.8682 0.3670 3.615
Concordance= 0.505 (se = 0.015 )
Rsquare= 0 (max possible= 0.957 )
Likelihood ratio test= 0.15 on 2 df, p=0.9298
Wald test = 0.15 on 2 df, p=0.9291
Score (logrank) test = 0.15 on 2 df, p=0.929Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: U_R_F
Class (treatment at performing facility)
uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)_________________________________________________
## CLASS_OF_CASE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CLASS_OF_CASE_F, data = data)
n events median 0.95LCL 0.95UCL
CLASS_OF_CASE_F=Other_Facility 41 7 NA 93.4 NA
CLASS_OF_CASE_F=All_Part_Prim 634 173 138 133.0 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CLASS_OF_CASE_F, data = data)
CLASS_OF_CASE_F=Other_Facility
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 20 5 0.827 0.0714 0.698 0.979
24 19 0 0.827 0.0714 0.698 0.979
36 16 0 0.827 0.0714 0.698 0.979
48 12 1 0.775 0.0835 0.628 0.958
60 11 0 0.775 0.0835 0.628 0.958
120 4 1 0.646 0.1370 0.426 0.979
CLASS_OF_CASE_F=All_Part_Prim
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 544 41 0.933 0.0101 0.914 0.953
24 478 23 0.892 0.0128 0.867 0.917
36 411 25 0.843 0.0154 0.813 0.874
48 347 18 0.804 0.0172 0.771 0.839
60 299 13 0.772 0.0187 0.737 0.810
120 75 45 0.594 0.0288 0.540 0.653
## Univariable Cox Proportional Hazard Model for: CLASS_OF_CASE_FLoglik converged before variable 1 ; beta may be infinite. Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CLASS_OF_CASE_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
CLASS_OF_CASE_FAll_Part_Prim -0.000415 0.999585 0.385995 -0.001 0.999
exp(coef) exp(-coef) lower .95 upper .95
CLASS_OF_CASE_FAll_Part_Prim 0.9996 1 0.4691 2.13
Concordance= 0.506 (se = 0.008 )
Rsquare= 0 (max possible= 0.956 )
Likelihood ratio test= 0 on 1 df, p=0.9991
Wald test = 0 on 1 df, p=0.9991
Score (logrank) test = 0 on 1 df, p=0.9991Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: CLASS_OF_CASE_F
Year
uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)_________________________________________________
## YEAR_OF_DIAGNOSIS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ YEAR_OF_DIAGNOSIS, data = data)
n events median 0.95LCL 0.95UCL
YEAR_OF_DIAGNOSIS=2004 45 16 153 134.1 NA
YEAR_OF_DIAGNOSIS=2005 63 20 NA 137.0 NA
YEAR_OF_DIAGNOSIS=2006 75 33 133 95.3 NA
YEAR_OF_DIAGNOSIS=2007 62 27 115 101.2 NA
YEAR_OF_DIAGNOSIS=2008 42 13 NA NA NA
YEAR_OF_DIAGNOSIS=2009 62 14 NA NA NA
YEAR_OF_DIAGNOSIS=2010 53 14 NA NA NA
YEAR_OF_DIAGNOSIS=2011 64 15 NA 73.8 NA
YEAR_OF_DIAGNOSIS=2012 47 8 NA 66.2 NA
YEAR_OF_DIAGNOSIS=2013 60 11 NA NA NA
YEAR_OF_DIAGNOSIS=2014 48 4 NA NA NA
YEAR_OF_DIAGNOSIS=2015 54 5 NA 28.4 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ YEAR_OF_DIAGNOSIS, data = data)
YEAR_OF_DIAGNOSIS=2004
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 36 6 0.862 0.0523 0.766 0.971
24 31 2 0.811 0.0606 0.700 0.939
36 28 3 0.732 0.0696 0.608 0.882
48 28 0 0.732 0.0696 0.608 0.882
60 26 1 0.706 0.0719 0.578 0.862
120 16 2 0.650 0.0764 0.516 0.818
YEAR_OF_DIAGNOSIS=2005
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 58 5 0.921 0.0341 0.856 0.990
24 49 3 0.870 0.0430 0.790 0.958
36 45 2 0.833 0.0484 0.744 0.934
48 43 0 0.833 0.0484 0.744 0.934
60 43 0 0.833 0.0484 0.744 0.934
120 28 6 0.702 0.0642 0.586 0.840
YEAR_OF_DIAGNOSIS=2006
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 67 4 0.946 0.0262 0.896 0.999
24 60 6 0.861 0.0409 0.784 0.945
36 57 2 0.832 0.0443 0.749 0.924
48 53 3 0.788 0.0487 0.698 0.889
60 52 0 0.788 0.0487 0.698 0.889
120 26 16 0.524 0.0630 0.414 0.663
YEAR_OF_DIAGNOSIS=2007
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 56 6 0.903 0.0375 0.833 0.980
24 54 1 0.887 0.0402 0.812 0.969
36 48 6 0.789 0.0521 0.693 0.898
48 43 2 0.756 0.0549 0.655 0.871
60 40 2 0.720 0.0579 0.615 0.843
120 9 10 0.496 0.0734 0.371 0.663
YEAR_OF_DIAGNOSIS=2008
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 37 4 0.902 0.0463 0.816 0.998
24 36 1 0.878 0.0511 0.783 0.984
36 34 0 0.878 0.0511 0.783 0.984
48 31 3 0.801 0.0632 0.686 0.935
60 30 1 0.775 0.0662 0.655 0.916
YEAR_OF_DIAGNOSIS=2009
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 54 3 0.950 0.0281 0.896 1.000
24 51 2 0.914 0.0368 0.845 0.989
36 47 0 0.914 0.0368 0.845 0.989
48 40 4 0.834 0.0509 0.740 0.940
60 36 1 0.812 0.0541 0.713 0.925
YEAR_OF_DIAGNOSIS=2010
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 41 3 0.941 0.0330 0.879 1.000
24 40 1 0.918 0.0394 0.844 0.999
36 37 3 0.849 0.0528 0.752 0.959
48 34 3 0.780 0.0617 0.668 0.911
60 31 2 0.735 0.0661 0.616 0.876
YEAR_OF_DIAGNOSIS=2011
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 59 2 0.968 0.0225 0.925 1.000
24 56 2 0.935 0.0316 0.875 0.999
36 50 4 0.867 0.0438 0.785 0.957
48 45 2 0.830 0.0490 0.740 0.932
60 39 4 0.753 0.0579 0.647 0.875
YEAR_OF_DIAGNOSIS=2012
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 41 2 0.953 0.0321 0.893 1.000
24 39 0 0.953 0.0321 0.893 1.000
36 34 3 0.879 0.0506 0.786 0.985
48 30 0 0.879 0.0506 0.786 0.985
60 13 2 0.821 0.0619 0.708 0.952
YEAR_OF_DIAGNOSIS=2013
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 45 5 0.907 0.0398 0.832 0.988
24 37 3 0.841 0.0521 0.744 0.949
36 34 1 0.817 0.0556 0.715 0.934
48 12 2 0.749 0.0704 0.622 0.900
YEAR_OF_DIAGNOSIS=2014
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 32 3 0.931 0.0385 0.859 1
24 28 1 0.899 0.0487 0.808 1
36 13 0 0.899 0.0487 0.808 1
YEAR_OF_DIAGNOSIS=2015
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 38 3 0.937 0.0354 0.870 1
24 16 1 0.907 0.0448 0.824 1
## Univariable Cox Proportional Hazard Model for: YEAR_OF_DIAGNOSISX matrix deemed to be singular; variable 12Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ YEAR_OF_DIAGNOSIS, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
YEAR_OF_DIAGNOSIS2005 -0.15599 0.85557 0.34183 -0.456 0.648
YEAR_OF_DIAGNOSIS2006 0.33535 1.39843 0.31703 1.058 0.290
YEAR_OF_DIAGNOSIS2007 0.36223 1.43653 0.33044 1.096 0.273
YEAR_OF_DIAGNOSIS2008 0.01742 1.01758 0.38687 0.045 0.964
YEAR_OF_DIAGNOSIS2009 -0.07927 0.92379 0.38114 -0.208 0.835
YEAR_OF_DIAGNOSIS2010 0.18053 1.19786 0.38188 0.473 0.636
YEAR_OF_DIAGNOSIS2011 0.11601 1.12301 0.37783 0.307 0.759
YEAR_OF_DIAGNOSIS2012 -0.06744 0.93479 0.44973 -0.150 0.881
YEAR_OF_DIAGNOSIS2013 0.32908 1.38968 0.41173 0.799 0.424
YEAR_OF_DIAGNOSIS2014 -0.22881 0.79548 0.57472 -0.398 0.691
YEAR_OF_DIAGNOSIS2015 0.08046 1.08379 0.53305 0.151 0.880
YEAR_OF_DIAGNOSIS2016 NA NA 0.00000 NA NA
exp(coef) exp(-coef) lower .95 upper .95
YEAR_OF_DIAGNOSIS2005 0.8556 1.1688 0.4378 1.672
YEAR_OF_DIAGNOSIS2006 1.3984 0.7151 0.7512 2.603
YEAR_OF_DIAGNOSIS2007 1.4365 0.6961 0.7517 2.745
YEAR_OF_DIAGNOSIS2008 1.0176 0.9827 0.4767 2.172
YEAR_OF_DIAGNOSIS2009 0.9238 1.0825 0.4377 1.950
YEAR_OF_DIAGNOSIS2010 1.1979 0.8348 0.5667 2.532
YEAR_OF_DIAGNOSIS2011 1.1230 0.8905 0.5355 2.355
YEAR_OF_DIAGNOSIS2012 0.9348 1.0698 0.3872 2.257
YEAR_OF_DIAGNOSIS2013 1.3897 0.7196 0.6201 3.114
YEAR_OF_DIAGNOSIS2014 0.7955 1.2571 0.2579 2.454
YEAR_OF_DIAGNOSIS2015 1.0838 0.9227 0.3812 3.081
YEAR_OF_DIAGNOSIS2016 NA NA NA NA
Concordance= 0.542 (se = 0.023 )
Rsquare= 0.01 (max possible= 0.956 )
Likelihood ratio test= 6.71 on 11 df, p=0.8218
Wald test = 6.66 on 11 df, p=0.8259
Score (logrank) test = 6.75 on 11 df, p=0.8187Removed 2 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: YEAR_OF_DIAGNOSISThis manual palette can handle a maximum of 10 values. You have supplied 12.
Histology
#uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)Grade
uni_var(test_var = "GRADE_F", data_imp = data)_________________________________________________
## GRADE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ GRADE_F, data = data)
n events median 0.95LCL 0.95UCL
GRADE_F=Gr I: Well Diff 22 7 132.9 93.4 NA
GRADE_F=Gr II: Mod Diff 55 14 NA 116.2 NA
GRADE_F=Gr III: Poor Diff 106 39 136.8 73.2 NA
GRADE_F=Gr IV: Undiff/Anaplastic 2 1 35.9 NA NA
GRADE_F=NA/Unkown 490 119 153.1 134.1 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ GRADE_F, data = data)
GRADE_F=Gr I: Well Diff
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 21 1 0.955 0.0444 0.871 1.000
24 18 1 0.904 0.0645 0.786 1.000
36 15 1 0.854 0.0781 0.714 1.000
48 14 0 0.854 0.0781 0.714 1.000
60 9 1 0.793 0.0933 0.630 0.999
120 2 2 0.566 0.1509 0.336 0.955
GRADE_F=Gr II: Mod Diff
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 49 4 0.925 0.0361 0.857 0.998
24 44 1 0.905 0.0404 0.829 0.988
36 36 4 0.820 0.0547 0.719 0.934
48 31 0 0.820 0.0547 0.719 0.934
60 27 0 0.820 0.0547 0.719 0.934
120 5 5 0.564 0.1223 0.369 0.863
GRADE_F=Gr III: Poor Diff
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 92 10 0.903 0.0292 0.848 0.962
24 81 7 0.833 0.0370 0.763 0.909
36 71 5 0.779 0.0417 0.702 0.865
48 57 7 0.701 0.0469 0.615 0.799
60 48 3 0.663 0.0492 0.573 0.767
120 13 6 0.557 0.0574 0.455 0.681
GRADE_F=Gr IV: Undiff/Anaplastic
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 1 0 1 0 1 1
GRADE_F=NA/Unkown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 400 31 0.933 0.0116 0.911 0.956
24 353 14 0.899 0.0143 0.871 0.928
36 305 14 0.862 0.0169 0.829 0.895
48 257 12 0.826 0.0191 0.789 0.864
60 226 9 0.795 0.0210 0.755 0.837
120 59 33 0.613 0.0336 0.551 0.683
## Univariable Cox Proportional Hazard Model for: GRADE_FX matrix deemed to be singular; variable 4 5 6 7Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ GRADE_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
GRADE_FGr II: Mod Diff -0.1592 0.8528 0.4640 -0.343 0.731
GRADE_FGr III: Poor Diff 0.2431 1.2752 0.4112 0.591 0.554
GRADE_FGr IV: Undiff/Anaplastic 1.2910 3.6363 1.0721 1.204 0.229
GRADE_F5 NA NA 0.0000 NA NA
GRADE_F6 NA NA 0.0000 NA NA
GRADE_F7 NA NA 0.0000 NA NA
GRADE_F8 NA NA 0.0000 NA NA
GRADE_FNA/Unkown -0.1821 0.8335 0.3898 -0.467 0.640
exp(coef) exp(-coef) lower .95 upper .95
GRADE_FGr II: Mod Diff 0.8528 1.1726 0.3435 2.117
GRADE_FGr III: Poor Diff 1.2752 0.7842 0.5696 2.855
GRADE_FGr IV: Undiff/Anaplastic 3.6363 0.2750 0.4447 29.735
GRADE_F5 NA NA NA NA
GRADE_F6 NA NA NA NA
GRADE_F7 NA NA NA NA
GRADE_F8 NA NA NA NA
GRADE_FNA/Unkown 0.8335 1.1998 0.3882 1.790
Concordance= 0.543 (se = 0.019 )
Rsquare= 0.009 (max possible= 0.956 )
Likelihood ratio test= 6.3 on 4 df, p=0.178
Wald test = 7.29 on 4 df, p=0.1215
Score (logrank) test = 7.7 on 4 df, p=0.1032Removed 5 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: GRADE_F
Clinical T Stage
#uni_var(test_var = "TNM_CLIN_T", data_imp = data)Clinical N Stage
#uni_var(test_var = "TNM_CLIN_N", data_imp = data)Clinical M Stage
#uni_var(test_var = "TNM_CLIN_M", data_imp = data)Clinical Stage Group
uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)_________________________________________________
## TNM_CLIN_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_STAGE_GROUP, data = data)
n events median 0.95LCL 0.95UCL
TNM_CLIN_STAGE_GROUP=0 333 74 NA 138.22 NA
TNM_CLIN_STAGE_GROUP=1 39 5 NA 132.99 NA
TNM_CLIN_STAGE_GROUP=1A 32 3 NA NA NA
TNM_CLIN_STAGE_GROUP=1B 1 0 NA NA NA
TNM_CLIN_STAGE_GROUP=2A 23 6 NA NA NA
TNM_CLIN_STAGE_GROUP=2B 13 5 71.03 69.68 NA
TNM_CLIN_STAGE_GROUP=3 2 1 7.33 7.33 NA
TNM_CLIN_STAGE_GROUP=3A 7 3 116.17 50.99 NA
TNM_CLIN_STAGE_GROUP=3B 12 9 24.42 4.50 NA
TNM_CLIN_STAGE_GROUP=3C 3 2 42.15 4.21 NA
TNM_CLIN_STAGE_GROUP=4 27 22 14.00 8.34 26.6
TNM_CLIN_STAGE_GROUP=99 183 50 153.13 134.08 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_STAGE_GROUP, data = data)
TNM_CLIN_STAGE_GROUP=0
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 280 10 0.968 0.0101 0.948 0.988
24 250 11 0.928 0.0151 0.899 0.958
36 218 7 0.901 0.0179 0.867 0.937
48 175 14 0.839 0.0231 0.795 0.885
60 143 6 0.808 0.0255 0.759 0.859
120 25 25 0.585 0.0464 0.501 0.683
TNM_CLIN_STAGE_GROUP=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 38 0 1.000 0.0000 1.000 1
24 34 0 1.000 0.0000 1.000 1
36 32 0 1.000 0.0000 1.000 1
48 26 0 1.000 0.0000 1.000 1
60 24 2 0.923 0.0523 0.826 1
120 12 1 0.877 0.0670 0.755 1
TNM_CLIN_STAGE_GROUP=1A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 30 0 1.000 0.0000 1.000 1
24 26 0 1.000 0.0000 1.000 1
36 21 2 0.916 0.0567 0.812 1
48 19 0 0.916 0.0567 0.812 1
60 16 0 0.916 0.0567 0.812 1
TNM_CLIN_STAGE_GROUP=1B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
24 1 0 1 0 1 1
36 1 0 1 0 1 1
48 1 0 1 0 1 1
60 1 0 1 0 1 1
TNM_CLIN_STAGE_GROUP=2A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 19 4 0.826 0.0790 0.685 0.996
24 15 1 0.774 0.0894 0.618 0.971
36 12 1 0.719 0.0986 0.550 0.941
48 11 0 0.719 0.0986 0.550 0.941
60 8 0 0.719 0.0986 0.550 0.941
120 2 0 0.719 0.0986 0.550 0.941
TNM_CLIN_STAGE_GROUP=2B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 11 1 0.923 0.0739 0.789 1
24 10 1 0.839 0.1045 0.657 1
36 8 1 0.746 0.1279 0.533 1
48 7 0 0.746 0.1279 0.533 1
60 6 0 0.746 0.1279 0.533 1
TNM_CLIN_STAGE_GROUP=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 1 0.5 0.354 0.125 1
24 1 0 0.5 0.354 0.125 1
36 1 0 0.5 0.354 0.125 1
TNM_CLIN_STAGE_GROUP=3A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 1 0.857 0.132 0.6334 1
24 6 0 0.857 0.132 0.6334 1
36 6 0 0.857 0.132 0.6334 1
48 5 0 0.857 0.132 0.6334 1
60 4 1 0.686 0.186 0.4026 1
120 1 1 0.343 0.260 0.0777 1
TNM_CLIN_STAGE_GROUP=3B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 5 0.583 0.142 0.362 0.941
24 6 1 0.500 0.144 0.284 0.880
36 5 1 0.417 0.142 0.213 0.814
48 3 1 0.333 0.136 0.150 0.742
60 3 0 0.333 0.136 0.150 0.742
TNM_CLIN_STAGE_GROUP=3C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 1 0.667 0.272 0.2995 1
24 2 0 0.667 0.272 0.2995 1
36 2 0 0.667 0.272 0.2995 1
48 1 1 0.333 0.272 0.0673 1
60 1 0 0.333 0.272 0.0673 1
TNM_CLIN_STAGE_GROUP=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 15 11 0.578 0.0968 0.4160 0.802
24 7 7 0.292 0.0914 0.1582 0.539
36 3 4 0.125 0.0672 0.0437 0.359
48 1 0 0.125 0.0672 0.0437 0.359
60 1 0 0.125 0.0672 0.0437 0.359
TNM_CLIN_STAGE_GROUP=99
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 154 12 0.932 0.0189 0.896 0.970
24 139 2 0.920 0.0206 0.880 0.961
36 118 9 0.857 0.0278 0.805 0.914
48 110 3 0.835 0.0298 0.779 0.896
60 103 4 0.804 0.0326 0.743 0.871
120 39 15 0.646 0.0457 0.562 0.742
## Univariable Cox Proportional Hazard Model for: TNM_CLIN_STAGE_GROUPLoglik converged before variable 3 ; beta may be infinite. X matrix deemed to be singular; variable 4 5 8 14 15 16 17 18 19Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_STAGE_GROUP, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
TNM_CLIN_STAGE_GROUP1 -9.685e-01 3.797e-01 4.634e-01 -2.090 0.0366 *
TNM_CLIN_STAGE_GROUP1A -7.286e-01 4.826e-01 5.899e-01 -1.235 0.2168
TNM_CLIN_STAGE_GROUP1B -1.306e+01 2.128e-06 1.445e+03 -0.009 0.9928
TNM_CLIN_STAGE_GROUP1C NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUP2 NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUP2A 2.323e-01 1.261e+00 4.249e-01 0.547 0.5846
TNM_CLIN_STAGE_GROUP2B 7.265e-01 2.068e+00 4.631e-01 1.569 0.1167
TNM_CLIN_STAGE_GROUP2C NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUP3 1.660e+00 5.260e+00 1.009e+00 1.645 0.1001
TNM_CLIN_STAGE_GROUP3A 5.081e-01 1.662e+00 5.896e-01 0.862 0.3888
TNM_CLIN_STAGE_GROUP3B 1.745e+00 5.729e+00 3.542e-01 4.928 8.29e-07 ***
TNM_CLIN_STAGE_GROUP3C 1.136e+00 3.115e+00 7.176e-01 1.583 0.1133
TNM_CLIN_STAGE_GROUP4 2.501e+00 1.220e+01 2.554e-01 9.793 < 2e-16 ***
TNM_CLIN_STAGE_GROUP4A NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUP4A1 NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUP4A2 NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUP4B NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUP4C NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUPN_A NA NA 0.000e+00 NA NA
TNM_CLIN_STAGE_GROUP99 -5.359e-02 9.478e-01 1.853e-01 -0.289 0.7725
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_STAGE_GROUP1 3.797e-01 2.634e+00 0.1531 0.9416
TNM_CLIN_STAGE_GROUP1A 4.826e-01 2.072e+00 0.1519 1.5334
TNM_CLIN_STAGE_GROUP1B 2.128e-06 4.699e+05 0.0000 Inf
TNM_CLIN_STAGE_GROUP1C NA NA NA NA
TNM_CLIN_STAGE_GROUP2 NA NA NA NA
TNM_CLIN_STAGE_GROUP2A 1.261e+00 7.927e-01 0.5485 2.9010
TNM_CLIN_STAGE_GROUP2B 2.068e+00 4.836e-01 0.8343 5.1252
TNM_CLIN_STAGE_GROUP2C NA NA NA NA
TNM_CLIN_STAGE_GROUP3 5.260e+00 1.901e-01 0.7273 38.0386
TNM_CLIN_STAGE_GROUP3A 1.662e+00 6.016e-01 0.5234 5.2791
TNM_CLIN_STAGE_GROUP3B 5.729e+00 1.746e-01 2.8614 11.4690
TNM_CLIN_STAGE_GROUP3C 3.115e+00 3.210e-01 0.7632 12.7132
TNM_CLIN_STAGE_GROUP4 1.220e+01 8.199e-02 7.3934 20.1202
TNM_CLIN_STAGE_GROUP4A NA NA NA NA
TNM_CLIN_STAGE_GROUP4A1 NA NA NA NA
TNM_CLIN_STAGE_GROUP4A2 NA NA NA NA
TNM_CLIN_STAGE_GROUP4B NA NA NA NA
TNM_CLIN_STAGE_GROUP4C NA NA NA NA
TNM_CLIN_STAGE_GROUPN_A NA NA NA NA
TNM_CLIN_STAGE_GROUP99 9.478e-01 1.055e+00 0.6591 1.3630
Concordance= 0.655 (se = 0.022 )
Rsquare= 0.132 (max possible= 0.956 )
Likelihood ratio test= 95.51 on 11 df, p=1.332e-15
Wald test = 134.5 on 11 df, p=0
Score (logrank) test = 211.1 on 11 df, p=0Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 10 rows containing missing values (geom_errorbar).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_CLIN_STAGE_GROUPThis manual palette can handle a maximum of 10 values. You have supplied 12.
Pathologic T Stage
uni_var(test_var = "TNM_PATH_T", data_imp = data)_________________________________________________
## TNM_PATH_T
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_T, data = data)
58 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
TNM_PATH_T=p0 8 0 NA NA NA
TNM_PATH_T=p1 13 2 NA 66.07 NA
TNM_PATH_T=p1A 17 1 NA NA NA
TNM_PATH_T=p1B 11 3 NA 71.85 NA
TNM_PATH_T=p1C 24 4 136.77 136.77 NA
TNM_PATH_T=p1MI 7 1 NA NA NA
TNM_PATH_T=p2 12 5 NA 26.87 NA
TNM_PATH_T=p3 2 0 NA NA NA
TNM_PATH_T=p4 1 1 14.92 NA NA
TNM_PATH_T=p4B 5 2 NA 2.10 NA
TNM_PATH_T=p4D 3 3 7.49 4.21 NA
TNM_PATH_T=pIS 261 44 NA 115.48 NA
TNM_PATH_T=pX 253 95 134.08 116.17 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_T, data = data)
58 observations deleted due to missingness
TNM_PATH_T=p0
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 0 1 0 1 1
24 6 0 1 0 1 1
36 6 0 1 0 1 1
48 4 0 1 0 1 1
60 4 0 1 0 1 1
120 1 0 1 0 1 1
TNM_PATH_T=p1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 11 0 1.000 0.000 1.000 1
24 10 0 1.000 0.000 1.000 1
36 10 0 1.000 0.000 1.000 1
48 7 0 1.000 0.000 1.000 1
60 6 1 0.857 0.132 0.633 1
120 1 1 0.643 0.210 0.338 1
TNM_PATH_T=p1A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 17 0 1.000 0.000 1.000 1
24 15 0 1.000 0.000 1.000 1
36 13 0 1.000 0.000 1.000 1
48 12 0 1.000 0.000 1.000 1
60 11 0 1.000 0.000 1.000 1
120 4 1 0.875 0.117 0.673 1
TNM_PATH_T=p1B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 10 0 1.0 0.0000 1.000 1
24 9 1 0.9 0.0949 0.732 1
36 8 1 0.8 0.1265 0.587 1
48 7 0 0.8 0.1265 0.587 1
60 5 0 0.8 0.1265 0.587 1
120 1 1 0.6 0.1975 0.315 1
TNM_PATH_T=p1C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 23 0 1.000 0.000 1.000 1
24 21 0 1.000 0.000 1.000 1
36 19 0 1.000 0.000 1.000 1
48 19 0 1.000 0.000 1.000 1
60 19 0 1.000 0.000 1.000 1
120 2 3 0.769 0.117 0.571 1
TNM_PATH_T=p1MI
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 0 1.000 0.000 1.000 1
24 6 0 1.000 0.000 1.000 1
36 4 1 0.833 0.152 0.583 1
48 3 0 0.833 0.152 0.583 1
60 1 0 0.833 0.152 0.583 1
TNM_PATH_T=p2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 9 3 0.750 0.125 0.541 1.000
24 8 0 0.750 0.125 0.541 1.000
36 7 1 0.656 0.140 0.432 0.997
48 7 0 0.656 0.140 0.432 0.997
60 6 0 0.656 0.140 0.432 0.997
120 1 1 0.525 0.162 0.286 0.962
TNM_PATH_T=p3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 2 0 1 0 1 1
36 2 0 1 0 1 1
48 1 0 1 0 1 1
60 1 0 1 0 1 1
TNM_PATH_T=p4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
TNM_PATH_T=p4B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 2 0.6 0.219 0.293 1
24 3 0 0.6 0.219 0.293 1
36 3 0 0.6 0.219 0.293 1
48 1 0 0.6 0.219 0.293 1
60 1 0 0.6 0.219 0.293 1
TNM_PATH_T=p4D
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.0000 1.0000 2.0000 0.3333 0.2722 0.0673 1.0000
TNM_PATH_T=pIS
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 222 6 0.975 0.00998 0.956 0.995
24 199 4 0.957 0.01328 0.931 0.984
36 168 7 0.922 0.01837 0.886 0.958
48 131 7 0.879 0.02369 0.833 0.926
60 105 5 0.841 0.02803 0.788 0.898
120 17 15 0.600 0.06265 0.489 0.736
TNM_PATH_T=pX
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 215 25 0.898 0.0193 0.861 0.937
24 190 14 0.837 0.0239 0.792 0.885
36 170 11 0.787 0.0268 0.736 0.842
48 154 10 0.740 0.0290 0.686 0.799
60 142 5 0.716 0.0301 0.659 0.777
120 52 23 0.567 0.0373 0.498 0.645
## Univariable Cox Proportional Hazard Model for: TNM_PATH_TLoglik converged before variable 1,12 ; beta may be infinite. X matrix deemed to be singular; variable 8 9 10 11 13 14 16 18 20 22Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_T, data = data)
n= 617, number of events= 161
(58 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TNM_PATH_Tp0 -1.565e+01 1.602e-07 1.563e+03 -0.010 0.99202
TNM_PATH_Tp1 -6.479e-01 5.231e-01 7.152e-01 -0.906 0.36499
TNM_PATH_Tp1A -2.046e+00 1.293e-01 1.006e+00 -2.034 0.04198 *
TNM_PATH_Tp1B -1.667e-01 8.464e-01 5.872e-01 -0.284 0.77646
TNM_PATH_Tp1C -8.101e-01 4.448e-01 5.111e-01 -1.585 0.11295
TNM_PATH_Tp1MI -4.957e-01 6.091e-01 1.007e+00 -0.492 0.62261
TNM_PATH_Tp2 3.693e-01 1.447e+00 4.599e-01 0.803 0.42194
TNM_PATH_Tp2A NA NA 0.000e+00 NA NA
TNM_PATH_Tp2B NA NA 0.000e+00 NA NA
TNM_PATH_Tp2C NA NA 0.000e+00 NA NA
TNM_PATH_Tp2D NA NA 0.000e+00 NA NA
TNM_PATH_Tp3 -1.568e+01 1.545e-07 3.344e+03 -0.005 0.99626
TNM_PATH_Tp3A NA NA 0.000e+00 NA NA
TNM_PATH_Tp3B NA NA 0.000e+00 NA NA
TNM_PATH_Tp4 2.385e+00 1.086e+01 1.016e+00 2.348 0.01888 *
TNM_PATH_Tp4A NA NA 0.000e+00 NA NA
TNM_PATH_Tp4B 6.950e-01 2.004e+00 7.160e-01 0.971 0.33176
TNM_PATH_Tp4C NA NA 0.000e+00 NA NA
TNM_PATH_Tp4D 2.867e+00 1.758e+01 6.093e-01 4.705 2.54e-06 ***
TNM_PATH_TpA NA NA 0.000e+00 NA NA
TNM_PATH_TpIS -5.420e-01 5.816e-01 1.846e-01 -2.936 0.00332 **
TNM_PATH_TpX NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Tp0 1.602e-07 6.241e+06 0.00000 Inf
TNM_PATH_Tp1 5.231e-01 1.912e+00 0.12876 2.1253
TNM_PATH_Tp1A 1.293e-01 7.733e+00 0.01801 0.9285
TNM_PATH_Tp1B 8.464e-01 1.181e+00 0.26780 2.6754
TNM_PATH_Tp1C 4.448e-01 2.248e+00 0.16337 1.2112
TNM_PATH_Tp1MI 6.091e-01 1.642e+00 0.08459 4.3862
TNM_PATH_Tp2 1.447e+00 6.912e-01 0.58740 3.5632
TNM_PATH_Tp2A NA NA NA NA
TNM_PATH_Tp2B NA NA NA NA
TNM_PATH_Tp2C NA NA NA NA
TNM_PATH_Tp2D NA NA NA NA
TNM_PATH_Tp3 1.545e-07 6.474e+06 0.00000 Inf
TNM_PATH_Tp3A NA NA NA NA
TNM_PATH_Tp3B NA NA NA NA
TNM_PATH_Tp4 1.086e+01 9.212e-02 1.48296 79.4657
TNM_PATH_Tp4A NA NA NA NA
TNM_PATH_Tp4B 2.004e+00 4.991e-01 0.49242 8.1526
TNM_PATH_Tp4C NA NA NA NA
TNM_PATH_Tp4D 1.758e+01 5.689e-02 5.32478 58.0210
TNM_PATH_TpA NA NA NA NA
TNM_PATH_TpIS 5.816e-01 1.719e+00 0.40504 0.8351
TNM_PATH_TpX NA NA NA NA
Concordance= 0.643 (se = 0.023 )
Rsquare= 0.066 (max possible= 0.95 )
Likelihood ratio test= 41.83 on 12 df, p=3.557e-05
Wald test = 48.62 on 12 df, p=2.439e-06
Score (logrank) test = 90.09 on 12 df, p=4.752e-14Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 11 rows containing missing values (geom_errorbar).Removed 23 rows containing missing values (geom_text).Removed 23 rows containing missing values (geom_text).Removed 23 rows containing missing values (geom_text).Removed 23 rows containing missing values (geom_text).Removed 23 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_PATH_TThis manual palette can handle a maximum of 10 values. You have supplied 13.
Pathologic N Stage
uni_var(test_var = "TNM_PATH_N", data_imp = data)_________________________________________________
## TNM_PATH_N
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_N, data = data)
79 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
TNM_PATH_N=p0 233 36 NA NA NA
TNM_PATH_N=p0I- 30 3 NA NA NA
TNM_PATH_N=p0I+ 2 1 73.79 NA NA
TNM_PATH_N=p1 7 1 NA NA NA
TNM_PATH_N=p1A 9 3 75.17 26.74 NA
TNM_PATH_N=p1MI 2 0 NA NA NA
TNM_PATH_N=p2 2 1 12.06 12.06 NA
TNM_PATH_N=p2A 2 0 NA NA NA
TNM_PATH_N=p3 2 2 7.82 7.49 NA
TNM_PATH_N=p3A 3 2 18.79 10.05 NA
TNM_PATH_N=pX 304 109 134.08 116.17 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_N, data = data)
79 observations deleted due to missingness
TNM_PATH_N=p0
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 207 3 0.987 0.00766 0.972 1.000
24 194 1 0.982 0.00908 0.964 1.000
36 169 7 0.945 0.01612 0.914 0.977
48 140 4 0.920 0.02003 0.882 0.960
60 121 4 0.893 0.02361 0.848 0.940
120 21 16 0.682 0.05443 0.583 0.797
TNM_PATH_N=p0I-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 23 2 0.923 0.0523 0.826 1
24 19 1 0.877 0.0670 0.755 1
36 17 0 0.877 0.0670 0.755 1
48 15 0 0.877 0.0670 0.755 1
60 10 0 0.877 0.0670 0.755 1
TNM_PATH_N=p0I+
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 2 0 1 0 1 1
36 2 0 1 0 1 1
48 1 0 1 0 1 1
60 1 0 1 0 1 1
TNM_PATH_N=p1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 1 0.857 0.132 0.633 1
24 6 0 0.857 0.132 0.633 1
36 6 0 0.857 0.132 0.633 1
48 6 0 0.857 0.132 0.633 1
60 6 0 0.857 0.132 0.633 1
120 2 0 0.857 0.132 0.633 1
TNM_PATH_N=p1A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 1 0.889 0.105 0.706 1
24 7 0 0.889 0.105 0.706 1
36 5 1 0.741 0.161 0.484 1
48 4 0 0.741 0.161 0.484 1
60 3 0 0.741 0.161 0.484 1
TNM_PATH_N=p1MI
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 2 0 1 0 1 1
36 2 0 1 0 1 1
48 2 0 1 0 1 1
60 1 0 1 0 1 1
TNM_PATH_N=p2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1.0 0.000 1.000 1
24 1 1 0.5 0.354 0.125 1
36 1 0 0.5 0.354 0.125 1
TNM_PATH_N=p2A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 2 0 1 0 1 1
36 2 0 1 0 1 1
48 1 0 1 0 1 1
60 1 0 1 0 1 1
TNM_PATH_N=p3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
TNM_PATH_N=p3A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.000 2.000 1.000 0.667 0.272 0.300 1.000
TNM_PATH_N=pX
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 254 28 0.904 0.0172 0.871 0.939
24 220 17 0.842 0.0217 0.800 0.885
36 193 13 0.790 0.0247 0.743 0.840
48 169 11 0.744 0.0269 0.693 0.798
60 154 7 0.712 0.0283 0.659 0.770
120 56 26 0.556 0.0356 0.491 0.631
## Univariable Cox Proportional Hazard Model for: TNM_PATH_NLoglik converged before variable 10,12 ; beta may be infinite. X matrix deemed to be singular; variable 4 5 8 9 13 14 17 18 19 20Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_N, data = data)
n= 596, number of events= 158
(79 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TNM_PATH_Np0 -7.948e-01 4.517e-01 1.931e-01 -4.115 3.87e-05 ***
TNM_PATH_Np0I- -8.792e-01 4.151e-01 5.868e-01 -1.498 0.134076
TNM_PATH_Np0I+ 5.249e-01 1.690e+00 1.006e+00 0.522 0.601899
TNM_PATH_Np0M- NA NA 0.000e+00 NA NA
TNM_PATH_Np0M+ NA NA 0.000e+00 NA NA
TNM_PATH_Np1 -1.091e+00 3.358e-01 1.005e+00 -1.086 0.277440
TNM_PATH_Np1A 2.567e-01 1.293e+00 5.866e-01 0.438 0.661700
TNM_PATH_Np1B NA NA 0.000e+00 NA NA
TNM_PATH_Np1C NA NA 0.000e+00 NA NA
TNM_PATH_Np1MI -1.466e+01 4.280e-07 1.984e+03 -0.007 0.994102
TNM_PATH_Np2 1.150e+00 3.157e+00 1.008e+00 1.140 0.254168
TNM_PATH_Np2A -1.466e+01 4.287e-07 1.981e+03 -0.007 0.994094
TNM_PATH_Np2B NA NA 0.000e+00 NA NA
TNM_PATH_Np2C NA NA 0.000e+00 NA NA
TNM_PATH_Np3 2.826e+00 1.688e+01 7.363e-01 3.838 0.000124 ***
TNM_PATH_Np3A 1.862e+00 6.436e+00 7.244e-01 2.570 0.010163 *
TNM_PATH_Np3B NA NA 0.000e+00 NA NA
TNM_PATH_Np3C NA NA 0.000e+00 NA NA
TNM_PATH_Np4 NA NA 0.000e+00 NA NA
TNM_PATH_NpX NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Np0 4.517e-01 2.214e+00 0.30931 0.6595
TNM_PATH_Np0I- 4.151e-01 2.409e+00 0.13142 1.3112
TNM_PATH_Np0I+ 1.690e+00 5.916e-01 0.23526 12.1436
TNM_PATH_Np0M- NA NA NA NA
TNM_PATH_Np0M+ NA NA NA NA
TNM_PATH_Np1 3.358e-01 2.978e+00 0.04685 2.4064
TNM_PATH_Np1A 1.293e+00 7.736e-01 0.40942 4.0810
TNM_PATH_Np1B NA NA NA NA
TNM_PATH_Np1C NA NA NA NA
TNM_PATH_Np1MI 4.280e-07 2.336e+06 0.00000 Inf
TNM_PATH_Np2 3.157e+00 3.168e-01 0.43764 22.7714
TNM_PATH_Np2A 4.287e-07 2.333e+06 0.00000 Inf
TNM_PATH_Np2B NA NA NA NA
TNM_PATH_Np2C NA NA NA NA
TNM_PATH_Np3 1.688e+01 5.924e-02 3.98686 71.4626
TNM_PATH_Np3A 6.436e+00 1.554e-01 1.55593 26.6194
TNM_PATH_Np3B NA NA NA NA
TNM_PATH_Np3C NA NA NA NA
TNM_PATH_Np4 NA NA NA NA
TNM_PATH_NpX NA NA NA NA
Concordance= 0.626 (se = 0.023 )
Rsquare= 0.063 (max possible= 0.952 )
Likelihood ratio test= 38.9 on 10 df, p=2.645e-05
Wald test = 46.22 on 10 df, p=1.307e-06
Score (logrank) test = 73.18 on 10 df, p=1.076e-11Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 11 rows containing missing values (geom_errorbar).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_PATH_NThis manual palette can handle a maximum of 10 values. You have supplied 11.
Pathologic M Stage
uni_var(test_var = "TNM_PATH_M", data_imp = data)_________________________________________________
## TNM_PATH_M
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_M, data = data)
323 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
TNM_PATH_M=p1 8 6 18.6 14 NA
TNM_PATH_M=pX 344 118 153.1 134 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_M, data = data)
323 observations deleted due to missingness
TNM_PATH_M=p1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 2 0.750 0.153 0.5027 1.000
24 3 3 0.375 0.171 0.1533 0.917
36 2 1 0.250 0.153 0.0753 0.830
48 1 0 0.250 0.153 0.0753 0.830
60 1 0 0.250 0.153 0.0753 0.830
TNM_PATH_M=pX
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 305 26 0.923 0.0145 0.895 0.952
24 280 13 0.883 0.0177 0.849 0.918
36 259 12 0.844 0.0201 0.806 0.884
48 238 12 0.805 0.0222 0.762 0.849
60 227 5 0.788 0.0230 0.744 0.834
120 79 42 0.607 0.0311 0.549 0.671
## Univariable Cox Proportional Hazard Model for: TNM_PATH_MX matrix deemed to be singular; variable 1 3 4 5 6Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_M, data = data)
n= 352, number of events= 124
(323 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TNM_PATH_Mp0 NA NA 0.0000 NA NA
TNM_PATH_Mp1 1.8208 6.1768 0.4287 4.247 2.16e-05 ***
TNM_PATH_Mp1A NA NA 0.0000 NA NA
TNM_PATH_Mp1B NA NA 0.0000 NA NA
TNM_PATH_Mp1C NA NA 0.0000 NA NA
TNM_PATH_MpX NA NA 0.0000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Mp0 NA NA NA NA
TNM_PATH_Mp1 6.177 0.1619 2.666 14.31
TNM_PATH_Mp1A NA NA NA NA
TNM_PATH_Mp1B NA NA NA NA
TNM_PATH_Mp1C NA NA NA NA
TNM_PATH_MpX NA NA NA NA
Concordance= 0.526 (se = 0.005 )
Rsquare= 0.032 (max possible= 0.977 )
Likelihood ratio test= 11.38 on 1 df, p=0.0007442
Wald test = 18.04 on 1 df, p=2.162e-05
Score (logrank) test = 23.59 on 1 df, p=1.193e-06Removed 6 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_PATH_M
Pathologic Stage Group
uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)_________________________________________________
## TNM_PATH_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_STAGE_GROUP, data = data)
40 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
TNM_PATH_STAGE_GROUP=0 298 58 153.1 138.22 NA
TNM_PATH_STAGE_GROUP=1 55 10 NA 136.77 NA
TNM_PATH_STAGE_GROUP=1A 26 2 NA 73.79 NA
TNM_PATH_STAGE_GROUP=1B 2 0 NA NA NA
TNM_PATH_STAGE_GROUP=2 3 0 NA NA NA
TNM_PATH_STAGE_GROUP=2A 21 6 NA NA NA
TNM_PATH_STAGE_GROUP=2B 8 2 132.9 75.17 NA
TNM_PATH_STAGE_GROUP=3A 4 2 93.6 71.03 NA
TNM_PATH_STAGE_GROUP=3B 6 2 NA 19.65 NA
TNM_PATH_STAGE_GROUP=3C 5 4 10.1 8.15 NA
TNM_PATH_STAGE_GROUP=4 9 7 14.9 12.06 NA
TNM_PATH_STAGE_GROUP=99 198 77 107.9 83.94 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_STAGE_GROUP, data = data)
40 observations deleted due to missingness
TNM_PATH_STAGE_GROUP=0
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 258 7 0.975 0.00936 0.957 0.993
24 239 3 0.963 0.01139 0.941 0.986
36 209 7 0.934 0.01556 0.904 0.965
48 172 9 0.890 0.02059 0.851 0.931
60 144 7 0.850 0.02452 0.804 0.900
120 33 22 0.636 0.04660 0.551 0.734
TNM_PATH_STAGE_GROUP=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 53 0 1.000 0.0000 1.000 1.000
24 51 0 1.000 0.0000 1.000 1.000
36 50 0 1.000 0.0000 1.000 1.000
48 43 1 0.979 0.0206 0.940 1.000
60 40 2 0.934 0.0371 0.864 1.000
120 15 6 0.768 0.0694 0.643 0.917
TNM_PATH_STAGE_GROUP=1A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 24 0 1.00 0.0000 1.000 1
24 21 0 1.00 0.0000 1.000 1
36 16 1 0.95 0.0487 0.859 1
48 15 0 0.95 0.0487 0.859 1
60 11 0 0.95 0.0487 0.859 1
TNM_PATH_STAGE_GROUP=1B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 2 0 1 0 1 1
36 2 0 1 0 1 1
48 2 0 1 0 1 1
60 1 0 1 0 1 1
TNM_PATH_STAGE_GROUP=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 0 1 0 1 1
24 3 0 1 0 1 1
36 3 0 1 0 1 1
48 3 0 1 0 1 1
60 3 0 1 0 1 1
120 1 0 1 0 1 1
TNM_PATH_STAGE_GROUP=2A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 19 2 0.905 0.0641 0.788 1.000
24 17 1 0.854 0.0778 0.715 1.000
36 13 3 0.694 0.1046 0.517 0.933
48 13 0 0.694 0.1046 0.517 0.933
60 12 0 0.694 0.1046 0.517 0.933
120 4 0 0.694 0.1046 0.517 0.933
TNM_PATH_STAGE_GROUP=2B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 0 1.000 0.000 1.0 1
24 6 0 1.000 0.000 1.0 1
36 6 0 1.000 0.000 1.0 1
48 5 0 1.000 0.000 1.0 1
60 5 0 1.000 0.000 1.0 1
120 1 1 0.667 0.272 0.3 1
TNM_PATH_STAGE_GROUP=3A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 4 0 1 0 1 1
24 4 0 1 0 1 1
36 4 0 1 0 1 1
48 3 0 1 0 1 1
60 3 0 1 0 1 1
TNM_PATH_STAGE_GROUP=3B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 5 1 0.833 0.152 0.583 1
24 4 1 0.667 0.192 0.379 1
36 4 0 0.667 0.192 0.379 1
48 2 0 0.667 0.192 0.379 1
60 2 0 0.667 0.192 0.379 1
TNM_PATH_STAGE_GROUP=3C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.000 2.000 3.000 0.400 0.219 0.137 1.000
TNM_PATH_STAGE_GROUP=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 2 0.778 0.139 0.5485 1.000
24 3 4 0.333 0.157 0.1323 0.840
36 2 1 0.222 0.139 0.0655 0.754
48 1 0 0.222 0.139 0.0655 0.754
60 1 0 0.222 0.139 0.0655 0.754
TNM_PATH_STAGE_GROUP=99
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 149 29 0.846 0.0264 0.795 0.899
24 125 11 0.780 0.0310 0.721 0.843
36 104 10 0.713 0.0348 0.648 0.784
48 90 7 0.664 0.0370 0.595 0.740
60 80 4 0.633 0.0383 0.562 0.713
120 25 13 0.498 0.0457 0.416 0.596
## Univariable Cox Proportional Hazard Model for: TNM_PATH_STAGE_GROUPLoglik converged before variable 3,5 ; beta may be infinite. X matrix deemed to be singular; variable 4 8 9 14 15 16 17 18Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_STAGE_GROUP, data = data)
n= 635, number of events= 170
(40 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TNM_PATH_STAGE_GROUP1 -4.050e-01 6.670e-01 3.430e-01 -1.181 0.238
TNM_PATH_STAGE_GROUP1A -6.421e-01 5.262e-01 7.204e-01 -0.891 0.373
TNM_PATH_STAGE_GROUP1B -1.555e+01 1.772e-07 4.039e+03 -0.004 0.997
TNM_PATH_STAGE_GROUP1C NA NA 0.000e+00 NA NA
TNM_PATH_STAGE_GROUP2 -1.545e+01 1.948e-07 2.619e+03 -0.006 0.995
TNM_PATH_STAGE_GROUP2A 2.827e-01 1.327e+00 4.294e-01 0.658 0.510
TNM_PATH_STAGE_GROUP2B 2.345e-01 1.264e+00 7.196e-01 0.326 0.745
TNM_PATH_STAGE_GROUP2C NA NA 0.000e+00 NA NA
TNM_PATH_STAGE_GROUP3 NA NA 0.000e+00 NA NA
TNM_PATH_STAGE_GROUP3A 8.199e-01 2.270e+00 7.198e-01 1.139 0.255
TNM_PATH_STAGE_GROUP3B 7.690e-01 2.158e+00 7.199e-01 1.068 0.285
TNM_PATH_STAGE_GROUP3C 2.870e+00 1.764e+01 5.315e-01 5.400 6.68e-08 ***
TNM_PATH_STAGE_GROUP4 2.337e+00 1.035e+01 4.062e-01 5.753 8.76e-09 ***
TNM_PATH_STAGE_GROUP4A NA NA 0.000e+00 NA NA
TNM_PATH_STAGE_GROUP4A1 NA NA 0.000e+00 NA NA
TNM_PATH_STAGE_GROUP4B NA NA 0.000e+00 NA NA
TNM_PATH_STAGE_GROUP4C NA NA 0.000e+00 NA NA
TNM_PATH_STAGE_GROUPN_A NA NA 0.000e+00 NA NA
TNM_PATH_STAGE_GROUP99 8.488e-01 2.337e+00 1.747e-01 4.859 1.18e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_STAGE_GROUP1 6.670e-01 1.499e+00 0.3405 1.306
TNM_PATH_STAGE_GROUP1A 5.262e-01 1.900e+00 0.1282 2.160
TNM_PATH_STAGE_GROUP1B 1.772e-07 5.642e+06 0.0000 Inf
TNM_PATH_STAGE_GROUP1C NA NA NA NA
TNM_PATH_STAGE_GROUP2 1.948e-07 5.132e+06 0.0000 Inf
TNM_PATH_STAGE_GROUP2A 1.327e+00 7.538e-01 0.5718 3.078
TNM_PATH_STAGE_GROUP2B 1.264e+00 7.910e-01 0.3085 5.180
TNM_PATH_STAGE_GROUP2C NA NA NA NA
TNM_PATH_STAGE_GROUP3 NA NA NA NA
TNM_PATH_STAGE_GROUP3A 2.270e+00 4.405e-01 0.5539 9.305
TNM_PATH_STAGE_GROUP3B 2.158e+00 4.635e-01 0.5262 8.847
TNM_PATH_STAGE_GROUP3C 1.764e+01 5.669e-02 6.2231 49.993
TNM_PATH_STAGE_GROUP4 1.035e+01 9.662e-02 4.6684 22.945
TNM_PATH_STAGE_GROUP4A NA NA NA NA
TNM_PATH_STAGE_GROUP4A1 NA NA NA NA
TNM_PATH_STAGE_GROUP4B NA NA NA NA
TNM_PATH_STAGE_GROUP4C NA NA NA NA
TNM_PATH_STAGE_GROUPN_A NA NA NA NA
TNM_PATH_STAGE_GROUP99 2.337e+00 4.279e-01 1.6593 3.291
Concordance= 0.691 (se = 0.023 )
Rsquare= 0.099 (max possible= 0.955 )
Likelihood ratio test= 65.86 on 11 df, p=7.419e-10
Wald test = 76.85 on 11 df, p=5.982e-12
Score (logrank) test = 109.2 on 11 df, p=0Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 9 rows containing missing values (geom_errorbar).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_PATH_STAGE_GROUPThis manual palette can handle a maximum of 10 values. You have supplied 12.
Margins
uni_var(test_var = "MARGINS", data_imp = data)_________________________________________________
## MARGINS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ MARGINS, data = data)
n events median 0.95LCL 0.95UCL
MARGINS=No Residual 511 106 153.1 137.0 NA
MARGINS=Residual, NOS 10 2 NA NA NA
MARGINS=Microscopic Resid 11 4 98.3 23.1 NA
MARGINS=Macroscopic Resid 3 2 112.3 108.5 NA
MARGINS=No surg 122 62 35.5 22.4 51
MARGINS=Unknown 18 4 NA NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ MARGINS, data = data)
MARGINS=No Residual
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 459 11 0.978 0.0067 0.964 0.991
24 414 9 0.958 0.0093 0.940 0.976
36 368 15 0.922 0.0128 0.897 0.947
48 315 12 0.890 0.0153 0.860 0.920
60 270 11 0.857 0.0177 0.823 0.892
120 70 40 0.671 0.0309 0.614 0.735
MARGINS=Residual, NOS
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 1 0.889 0.105 0.706 1
24 7 1 0.778 0.139 0.549 1
36 5 0 0.778 0.139 0.549 1
48 5 0 0.778 0.139 0.549 1
60 5 0 0.778 0.139 0.549 1
MARGINS=Microscopic Resid
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 11 0 1.000 0.000 1.0000 1
24 7 3 0.707 0.143 0.4758 1
36 6 0 0.707 0.143 0.4758 1
48 5 0 0.707 0.143 0.4758 1
60 4 0 0.707 0.143 0.4758 1
120 1 1 0.354 0.260 0.0836 1
MARGINS=Macroscopic Resid
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 0 1 0 1 1
24 3 0 1 0 1 1
36 3 0 1 0 1 1
48 3 0 1 0 1 1
60 3 0 1 0 1 1
MARGINS=No surg
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 68 33 0.694 0.0446 0.612 0.787
24 51 10 0.584 0.0494 0.495 0.689
36 33 9 0.466 0.0530 0.373 0.582
48 23 5 0.389 0.0543 0.296 0.512
60 21 2 0.356 0.0546 0.263 0.480
120 4 3 0.265 0.0613 0.169 0.417
MARGINS=Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 15 1 0.944 0.054 0.844 1
24 15 0 0.944 0.054 0.844 1
36 12 1 0.881 0.079 0.739 1
48 8 2 0.712 0.125 0.504 1
60 7 0 0.712 0.125 0.504 1
120 4 0 0.712 0.125 0.504 1
## Univariable Cox Proportional Hazard Model for: MARGINSX matrix deemed to be singular; variable 4Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ MARGINS, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
MARGINSResidual, NOS 0.23881 1.26974 0.71426 0.334 0.7381
MARGINSMicroscopic Resid 0.85753 2.35734 0.51000 1.681 0.0927 .
MARGINSMacroscopic Resid 0.70466 2.02316 0.71470 0.986 0.3242
MARGINSNot evaluable NA NA 0.00000 NA NA
MARGINSNo surg 1.75769 5.79900 0.16369 10.738 <2e-16 ***
MARGINSUnknown 0.01931 1.01950 0.51184 0.038 0.9699
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
MARGINSResidual, NOS 1.270 0.7876 0.3131 5.149
MARGINSMicroscopic Resid 2.357 0.4242 0.8676 6.405
MARGINSMacroscopic Resid 2.023 0.4943 0.4985 8.211
MARGINSNot evaluable NA NA NA NA
MARGINSNo surg 5.799 0.1724 4.2075 7.993
MARGINSUnknown 1.020 0.9809 0.3739 2.780
Concordance= 0.687 (se = 0.016 )
Rsquare= 0.131 (max possible= 0.956 )
Likelihood ratio test= 94.41 on 5 df, p=0
Wald test = 117.1 on 5 df, p=0
Score (logrank) test = 147.3 on 5 df, p=0Removed 2 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: MARGINS
Margins Yes/No
#uni_var(test_var = "MARGINS_YN", data_imp = data)30 Day Readmission
uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)_________________________________________________
## READM_HOSP_30_DAYS_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ READM_HOSP_30_DAYS_F, data = data)
n events median 0.95LCL 0.95UCL
READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 627 168 153.1 132.9 NA
READM_HOSP_30_DAYS_F=Unplan_Readmit_Same 15 5 83.9 74.3 NA
READM_HOSP_30_DAYS_F=Plan_Readmit_Same 18 3 137.0 137.0 NA
READM_HOSP_30_DAYS_F=PlanUnplan_Same 1 1 12.2 NA NA
READM_HOSP_30_DAYS_F=9 14 3 136.8 NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ READM_HOSP_30_DAYS_F, data = data)
READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 522 45 0.925 0.0108 0.904 0.946
24 457 22 0.884 0.0134 0.858 0.911
36 388 25 0.833 0.0160 0.802 0.865
48 324 17 0.794 0.0178 0.760 0.830
60 282 11 0.766 0.0192 0.729 0.804
120 73 42 0.585 0.0298 0.530 0.647
READM_HOSP_30_DAYS_F=Unplan_Readmit_Same
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 1 0.929 0.0688 0.803 1.000
24 12 0 0.929 0.0688 0.803 1.000
36 11 0 0.929 0.0688 0.803 1.000
48 11 0 0.929 0.0688 0.803 1.000
60 7 1 0.836 0.1077 0.649 1.000
120 1 3 0.418 0.1789 0.181 0.967
READM_HOSP_30_DAYS_F=Plan_Readmit_Same
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 18 0 1.000 0.0000 1.000 1
24 18 0 1.000 0.0000 1.000 1
36 18 0 1.000 0.0000 1.000 1
48 16 0 1.000 0.0000 1.000 1
60 13 1 0.938 0.0605 0.826 1
120 2 1 0.865 0.0890 0.707 1
READM_HOSP_30_DAYS_F=PlanUnplan_Same
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
READM_HOSP_30_DAYS_F=9
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 11 0 1.0 0.000 1.000 1
24 10 0 1.0 0.000 1.000 1
36 10 0 1.0 0.000 1.000 1
48 8 2 0.8 0.126 0.587 1
60 8 0 0.8 0.126 0.587 1
120 3 0 0.8 0.126 0.587 1
## Univariable Cox Proportional Hazard Model for: READM_HOSP_30_DAYS_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ READM_HOSP_30_DAYS_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
READM_HOSP_30_DAYS_FUnplan_Readmit_Same 0.1854 1.2038 0.4542 0.408 0.6830
READM_HOSP_30_DAYS_FPlan_Readmit_Same -0.8646 0.4212 0.5828 -1.484 0.1379
READM_HOSP_30_DAYS_FPlanUnplan_Same 2.5567 12.8929 1.0107 2.530 0.0114 *
READM_HOSP_30_DAYS_F9 -0.3725 0.6890 0.5829 -0.639 0.5228
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
READM_HOSP_30_DAYS_FUnplan_Readmit_Same 1.2038 0.83074 0.4943 2.932
READM_HOSP_30_DAYS_FPlan_Readmit_Same 0.4212 2.37415 0.1344 1.320
READM_HOSP_30_DAYS_FPlanUnplan_Same 12.8929 0.07756 1.7783 93.475
READM_HOSP_30_DAYS_F9 0.6890 1.45133 0.2198 2.160
Concordance= 0.521 (se = 0.011 )
Rsquare= 0.01 (max possible= 0.956 )
Likelihood ratio test= 6.85 on 4 df, p=0.1439
Wald test = 9.24 on 4 df, p=0.05537
Score (logrank) test = 13.9 on 4 df, p=0.007615Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: READM_HOSP_30_DAYS_F
Radiation Type
uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)_________________________________________________
## RX_SUMM_RADIATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_SUMM_RADIATION_F, data = data)
n events median 0.95LCL 0.95UCL
RX_SUMM_RADIATION_F=None 465 133 134 110.5 NA
RX_SUMM_RADIATION_F=Beam Radiation 200 44 NA 137.0 NA
RX_SUMM_RADIATION_F=Radioactive Implants 2 1 137 NA NA
RX_SUMM_RADIATION_F=Unknown 8 2 60 54.2 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_SUMM_RADIATION_F, data = data)
RX_SUMM_RADIATION_F=None
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 378 37 0.916 0.0132 0.890 0.942
24 337 14 0.880 0.0158 0.850 0.912
36 284 20 0.825 0.0190 0.789 0.863
48 239 13 0.785 0.0211 0.745 0.828
60 203 10 0.751 0.0228 0.707 0.797
120 45 34 0.549 0.0361 0.483 0.624
RX_SUMM_RADIATION_F=Beam Radiation
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 178 9 0.954 0.0150 0.925 0.984
24 153 9 0.903 0.0217 0.862 0.947
36 136 5 0.872 0.0250 0.825 0.923
48 115 6 0.832 0.0287 0.778 0.891
60 103 1 0.825 0.0295 0.769 0.884
120 32 12 0.686 0.0457 0.602 0.782
RX_SUMM_RADIATION_F=Radioactive Implants
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 2 0 1 0 1 1
36 2 0 1 0 1 1
48 2 0 1 0 1 1
60 2 0 1 0 1 1
120 1 0 1 0 1 1
RX_SUMM_RADIATION_F=Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 0 1.000 0.000 1.0000 1
24 5 0 1.000 0.000 1.0000 1
36 5 0 1.000 0.000 1.0000 1
48 3 0 1.000 0.000 1.0000 1
60 2 2 0.333 0.272 0.0673 1
120 1 0 0.333 0.272 0.0673 1
## Univariable Cox Proportional Hazard Model for: RX_SUMM_RADIATION_FX matrix deemed to be singular; variable 3 4 5Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_SUMM_RADIATION_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
RX_SUMM_RADIATION_FBeam Radiation -0.41462 0.66059 0.17430 -2.379 0.0174 *
RX_SUMM_RADIATION_FRadioactive Implants -0.18012 0.83517 1.00515 -0.179 0.8578
RX_SUMM_RADIATION_FRadioisotopes NA NA 0.00000 NA NA
RX_SUMM_RADIATION_FBeam + Imp or Isotopes NA NA 0.00000 NA NA
RX_SUMM_RADIATION_FRadiation, NOS NA NA 0.00000 NA NA
RX_SUMM_RADIATION_FUnknown 0.01881 1.01899 0.71316 0.026 0.9790
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
RX_SUMM_RADIATION_FBeam Radiation 0.6606 1.5138 0.4694 0.9296
RX_SUMM_RADIATION_FRadioactive Implants 0.8352 1.1974 0.1165 5.9890
RX_SUMM_RADIATION_FRadioisotopes NA NA NA NA
RX_SUMM_RADIATION_FBeam + Imp or Isotopes NA NA NA NA
RX_SUMM_RADIATION_FRadiation, NOS NA NA NA NA
RX_SUMM_RADIATION_FUnknown 1.0190 0.9814 0.2518 4.1230
Concordance= 0.538 (se = 0.019 )
Rsquare= 0.009 (max possible= 0.956 )
Likelihood ratio test= 6.08 on 3 df, p=0.108
Wald test = 5.69 on 3 df, p=0.1275
Score (logrank) test = 5.77 on 3 df, p=0.1231Removed 4 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: RX_SUMM_RADIATION_F
Lymphovascular Invasion
uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)_________________________________________________
## LYMPH_VASCULAR_INVASION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ LYMPH_VASCULAR_INVASION_F, data = data)
349 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 162 21 NA NA NA
LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv 10 1 NA 73.8 NA
LYMPH_VASCULAR_INVASION_F=Unknown 154 35 NA 69.7 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ LYMPH_VASCULAR_INVASION_F, data = data)
349 observations deleted due to missingness
LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 135 4 0.973 0.0133 0.947 0.999
24 114 2 0.957 0.0171 0.925 0.991
36 92 8 0.886 0.0289 0.831 0.945
48 64 3 0.847 0.0353 0.781 0.919
60 41 4 0.789 0.0434 0.708 0.879
LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 9 0 1 0 1 1
24 9 0 1 0 1 1
36 8 0 1 0 1 1
48 6 0 1 0 1 1
60 5 0 1 0 1 1
LYMPH_VASCULAR_INVASION_F=Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 112 14 0.899 0.0257 0.850 0.951
24 93 6 0.848 0.0315 0.789 0.912
36 68 4 0.808 0.0360 0.740 0.881
48 51 4 0.757 0.0418 0.679 0.843
60 37 4 0.685 0.0509 0.593 0.793
## Univariable Cox Proportional Hazard Model for: LYMPH_VASCULAR_INVASION_FX matrix deemed to be singular; variable 2Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ LYMPH_VASCULAR_INVASION_F, data = data)
n= 326, number of events= 57
(349 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv -0.5599 0.5713 1.0241 -0.547 0.58456
LYMPH_VASCULAR_INVASION_FN_A NA NA 0.0000 NA NA
LYMPH_VASCULAR_INVASION_FUnknown 0.7197 2.0538 0.2762 2.606 0.00916 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv 0.5713 1.7505 0.07676 4.252
LYMPH_VASCULAR_INVASION_FN_A NA NA NA NA
LYMPH_VASCULAR_INVASION_FUnknown 2.0538 0.4869 1.19534 3.529
Concordance= 0.617 (se = 0.036 )
Rsquare= 0.025 (max possible= 0.838 )
Likelihood ratio test= 8.27 on 2 df, p=0.01597
Wald test = 7.79 on 2 df, p=0.02036
Score (logrank) test = 8.23 on 2 df, p=0.01635Removed 2 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: LYMPH_VASCULAR_INVASION_F
Endoscopic/Robotic
uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)_________________________________________________
## RX_HOSP_SURG_APPR_2010_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
349 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
RX_HOSP_SURG_APPR_2010_F=No_Surg 96 29 69.7 47 NA
RX_HOSP_SURG_APPR_2010_F=Endo_Lap 1 1 29.6 NA NA
RX_HOSP_SURG_APPR_2010_F=Open_Unknown 229 27 NA NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
349 observations deleted due to missingness
RX_HOSP_SURG_APPR_2010_F=No_Surg
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 61 14 0.834 0.0406 0.758 0.918
24 44 5 0.755 0.0499 0.663 0.860
36 31 4 0.678 0.0580 0.573 0.802
48 20 3 0.601 0.0666 0.484 0.747
60 14 2 0.535 0.0739 0.408 0.702
RX_HOSP_SURG_APPR_2010_F=Endo_Lap
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
24 1 0 1 0 1 1
RX_HOSP_SURG_APPR_2010_F=Open_Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 194 4 0.981 0.00944 0.963 1.000
24 171 3 0.965 0.01288 0.940 0.991
36 137 7 0.923 0.01989 0.885 0.963
48 101 4 0.892 0.02472 0.844 0.941
60 69 6 0.829 0.03376 0.766 0.898
## Univariable Cox Proportional Hazard Model for: RX_HOSP_SURG_APPR_2010_FX matrix deemed to be singular; variable 1 2 4 6Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
n= 326, number of events= 57
(349 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
RX_HOSP_SURG_APPR_2010_FRobot_Assist NA NA 0.0000 NA NA
RX_HOSP_SURG_APPR_2010_FRobot_to_Open NA NA 0.0000 NA NA
RX_HOSP_SURG_APPR_2010_FEndo_Lap 1.1126 3.0421 1.0228 1.088 0.277
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open NA NA 0.0000 NA NA
RX_HOSP_SURG_APPR_2010_FOpen_Unknown -1.4090 0.2444 0.2693 -5.231 1.68e-07 ***
RX_HOSP_SURG_APPR_2010_FUnknown NA NA 0.0000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
RX_HOSP_SURG_APPR_2010_FRobot_Assist NA NA NA NA
RX_HOSP_SURG_APPR_2010_FRobot_to_Open NA NA NA NA
RX_HOSP_SURG_APPR_2010_FEndo_Lap 3.0421 0.3287 0.4098 22.5814
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open NA NA NA NA
RX_HOSP_SURG_APPR_2010_FOpen_Unknown 0.2444 4.0919 0.1441 0.4143
RX_HOSP_SURG_APPR_2010_FUnknown NA NA NA NA
Concordance= 0.689 (se = 0.03 )
Rsquare= 0.082 (max possible= 0.838 )
Likelihood ratio test= 28.04 on 2 df, p=8.138e-07
Wald test = 30.35 on 2 df, p=2.562e-07
Score (logrank) test = 36.8 on 2 df, p=1.023e-08Removed 5 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: RX_HOSP_SURG_APPR_2010_F
Surgery Radiation Sequence
uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)_________________________________________________
## SURG_RAD_SEQ
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURG_RAD_SEQ, data = data)
n events median 0.95LCL 0.95UCL
SURG_RAD_SEQ=Surg Alone 363 85 153.1 132.9 NA
SURG_RAD_SEQ=Surg then Rad 181 30 NA 136.8 NA
SURG_RAD_SEQ=Rad Alone 19 14 21.3 11.7 NA
SURG_RAD_SEQ=No Treatment 95 44 36.1 26.6 93.4
SURG_RAD_SEQ=Other 15 6 54.2 54.2 NA
SURG_RAD_SEQ=Rad then Surg 2 1 26.9 26.9 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURG_RAD_SEQ, data = data)
SURG_RAD_SEQ=Surg Alone
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 322 11 0.968 0.00944 0.950 0.987
24 293 8 0.943 0.01267 0.919 0.968
36 257 12 0.903 0.01661 0.871 0.936
48 222 8 0.873 0.01914 0.836 0.911
60 187 9 0.836 0.02204 0.794 0.880
120 44 32 0.612 0.03987 0.539 0.696
SURG_RAD_SEQ=Surg then Rad
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 166 2 0.989 0.00806 0.973 1.000
24 145 5 0.957 0.01587 0.927 0.989
36 131 3 0.937 0.01945 0.899 0.976
48 110 6 0.892 0.02569 0.843 0.944
60 99 0 0.892 0.02569 0.843 0.944
120 29 11 0.746 0.04811 0.658 0.847
SURG_RAD_SEQ=Rad Alone
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 7 0.632 0.111 0.4480 0.890
24 8 4 0.421 0.113 0.2485 0.713
36 6 1 0.361 0.112 0.1965 0.663
48 6 0 0.361 0.112 0.1965 0.663
60 5 1 0.301 0.108 0.1486 0.609
120 3 1 0.226 0.104 0.0913 0.557
SURG_RAD_SEQ=No Treatment
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 55 22 0.734 0.0489 0.644 0.836
24 42 6 0.645 0.0549 0.545 0.762
36 27 8 0.505 0.0615 0.398 0.641
48 17 5 0.402 0.0642 0.294 0.549
60 16 1 0.378 0.0646 0.270 0.529
120 1 2 0.284 0.0770 0.167 0.483
SURG_RAD_SEQ=Other
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 4 0.718 0.120 0.5177 0.996
24 7 0 0.718 0.120 0.5177 0.996
36 5 0 0.718 0.120 0.5177 0.996
48 3 0 0.718 0.120 0.5177 0.996
60 2 2 0.239 0.199 0.0467 1.000
120 1 0 0.239 0.199 0.0467 1.000
SURG_RAD_SEQ=Rad then Surg
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1.0 0.000 1.000 1
24 2 0 1.0 0.000 1.000 1
36 1 1 0.5 0.354 0.125 1
48 1 0 0.5 0.354 0.125 1
60 1 0 0.5 0.354 0.125 1
120 1 0 0.5 0.354 0.125 1
## Univariable Cox Proportional Hazard Model for: SURG_RAD_SEQX matrix deemed to be singular; variable 5Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURG_RAD_SEQ, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
SURG_RAD_SEQSurg then Rad -0.4087 0.6645 0.2127 -1.921 0.05469 .
SURG_RAD_SEQRad Alone 1.5325 4.6296 0.2902 5.280 1.29e-07 ***
SURG_RAD_SEQNo Treatment 1.5731 4.8218 0.1905 8.257 < 2e-16 ***
SURG_RAD_SEQOther 1.2840 3.6109 0.4243 3.026 0.00247 **
SURG_RAD_SEQRad before and after Surg NA NA 0.0000 NA NA
SURG_RAD_SEQRad then Surg 0.6271 1.8722 1.0067 0.623 0.53331
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad 0.6645 1.5049 0.4379 1.008
SURG_RAD_SEQRad Alone 4.6296 0.2160 2.6211 8.177
SURG_RAD_SEQNo Treatment 4.8218 0.2074 3.3192 7.004
SURG_RAD_SEQOther 3.6109 0.2769 1.5721 8.293
SURG_RAD_SEQRad before and after Surg NA NA NA NA
SURG_RAD_SEQRad then Surg 1.8722 0.5341 0.2603 13.467
Concordance= 0.695 (se = 0.021 )
Rsquare= 0.127 (max possible= 0.956 )
Likelihood ratio test= 91.79 on 5 df, p=0
Wald test = 110.1 on 5 df, p=0
Score (logrank) test = 137 on 5 df, p=0Removed 2 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: SURG_RAD_SEQ
Surgery Yes/No
uni_var(test_var = "SURGERY_YN", data_imp = data)_________________________________________________
## SURGERY_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURGERY_YN, data = data)
n events median 0.95LCL 0.95UCL
SURGERY_YN=No 115 58 35.6 24.25 59.7
SURGERY_YN=Ukn 9 4 NA 2.96 NA
SURGERY_YN=Yes 551 118 153.1 136.97 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURGERY_YN, data = data)
SURGERY_YN=No
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 67 29 0.713 0.0452 0.630 0.808
24 50 10 0.598 0.0506 0.507 0.706
36 33 9 0.477 0.0543 0.381 0.596
48 23 5 0.399 0.0556 0.303 0.524
60 21 2 0.364 0.0559 0.269 0.492
120 4 3 0.272 0.0628 0.173 0.427
SURGERY_YN=Ukn
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 4 0.556 0.166 0.31 0.997
24 2 0 0.556 0.166 0.31 0.997
SURGERY_YN=Yes
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 495 13 0.975 0.00675 0.962 0.989
24 445 13 0.949 0.00982 0.930 0.968
36 394 16 0.913 0.01284 0.888 0.939
48 336 14 0.879 0.01528 0.850 0.909
60 289 11 0.849 0.01730 0.815 0.883
120 75 43 0.656 0.03066 0.599 0.719
## Univariable Cox Proportional Hazard Model for: SURGERY_YN
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURGERY_YN, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
SURGERY_YNUkn 0.7669 2.1532 0.5228 1.467 0.142
SURGERY_YNYes -1.6801 0.1863 0.1639 -10.250 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
SURGERY_YNUkn 2.1532 0.4644 0.7728 5.999
SURGERY_YNYes 0.1863 5.3663 0.1351 0.257
Concordance= 0.675 (se = 0.013 )
Rsquare= 0.127 (max possible= 0.956 )
Likelihood ratio test= 91.81 on 2 df, p=0
Wald test = 116 on 2 df, p=0
Score (logrank) test = 147.9 on 2 df, p=0no non-missing arguments to min; returning Infno non-missing arguments to max; returning -InfTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: SURGERY_YN
Radiation Yes/No
uni_var(test_var = "RADIATION_YN", data_imp = data)_________________________________________________
## RADIATION_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RADIATION_YN, data = data)
7 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
RADIATION_YN=No 466 133 134 110 NA
RADIATION_YN=Yes 202 45 NA 137 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RADIATION_YN, data = data)
7 observations deleted due to missingness
RADIATION_YN=No
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 379 37 0.916 0.0132 0.891 0.942
24 337 14 0.881 0.0157 0.850 0.912
36 284 20 0.825 0.0190 0.789 0.864
48 239 13 0.785 0.0211 0.745 0.828
60 203 10 0.751 0.0228 0.707 0.797
120 45 34 0.549 0.0361 0.483 0.625
RADIATION_YN=Yes
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 180 9 0.954 0.0149 0.926 0.984
24 155 9 0.904 0.0215 0.863 0.947
36 138 5 0.874 0.0247 0.827 0.924
48 117 6 0.834 0.0284 0.781 0.892
60 105 1 0.827 0.0291 0.772 0.886
120 33 12 0.691 0.0450 0.608 0.785
## Univariable Cox Proportional Hazard Model for: RADIATION_YN
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RADIATION_YN, data = data)
n= 668, number of events= 178
(7 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
RADIATION_YNYes -0.4091 0.6642 0.1729 -2.367 0.0179 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
RADIATION_YNYes 0.6642 1.506 0.4733 0.9321
Concordance= 0.54 (se = 0.019 )
Rsquare= 0.009 (max possible= 0.955 )
Likelihood ratio test= 5.96 on 1 df, p=0.01462
Wald test = 5.6 on 1 df, p=0.01795
Score (logrank) test = 5.68 on 1 df, p=0.01717
## Unadjusted Kaplan Meier Overall Survival Curve for: RADIATION_YN
Chemo Yes/No
uni_var(test_var = "CHEMO_YN", data_imp = data)_________________________________________________
## CHEMO_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CHEMO_YN, data = data)
n events median 0.95LCL 0.95UCL
CHEMO_YN=No 563 142 153 134.1 NA
CHEMO_YN=Yes 74 27 137 88.7 NA
CHEMO_YN=Ukn 38 11 128 89.8 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CHEMO_YN, data = data)
CHEMO_YN=No
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 468 36 0.933 0.0108 0.912 0.954
24 417 14 0.904 0.0130 0.879 0.930
36 359 19 0.860 0.0157 0.830 0.892
48 301 16 0.820 0.0180 0.785 0.856
60 257 11 0.788 0.0196 0.751 0.828
120 66 40 0.603 0.0311 0.545 0.667
CHEMO_YN=Yes
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 64 8 0.890 0.0367 0.821 0.965
24 52 6 0.800 0.0480 0.711 0.900
36 45 5 0.723 0.0544 0.624 0.838
48 38 2 0.689 0.0569 0.586 0.810
60 36 0 0.689 0.0569 0.586 0.810
120 9 5 0.523 0.0834 0.383 0.715
CHEMO_YN=Ukn
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 32 2 0.944 0.0382 0.873 1.000
24 28 3 0.855 0.0601 0.745 0.981
36 23 1 0.819 0.0674 0.697 0.963
48 20 1 0.782 0.0739 0.650 0.941
60 17 2 0.697 0.0869 0.546 0.890
120 4 1 0.634 0.0995 0.466 0.862
## Univariable Cox Proportional Hazard Model for: CHEMO_YN
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CHEMO_YN, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
CHEMO_YNYes 0.3849 1.4694 0.2101 1.832 0.067 .
CHEMO_YNUkn 0.1531 1.1655 0.3132 0.489 0.625
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
CHEMO_YNYes 1.469 0.6805 0.9734 2.218
CHEMO_YNUkn 1.165 0.8580 0.6309 2.153
Concordance= 0.533 (se = 0.015 )
Rsquare= 0.005 (max possible= 0.956 )
Likelihood ratio test= 3.17 on 2 df, p=0.2046
Wald test = 3.44 on 2 df, p=0.179
Score (logrank) test = 3.48 on 2 df, p=0.1754Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: CHEMO_YN
Treatment Yes/No
uni_var(test_var = "Tx_YN", data_imp = data)_________________________________________________
## Tx_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ Tx_YN, data = data)
38 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
Tx_YN=FALSE 80 35 39.2 34.2 NA
Tx_YN=TRUE 557 134 153.1 136.8 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ Tx_YN, data = data)
38 observations deleted due to missingness
Tx_YN=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 48 16 0.769 0.0511 0.675 0.875
24 37 5 0.680 0.0586 0.574 0.805
36 25 6 0.556 0.0665 0.440 0.703
48 16 5 0.436 0.0707 0.318 0.600
60 15 1 0.409 0.0714 0.291 0.576
120 1 2 0.291 0.0894 0.159 0.531
Tx_YN=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 484 28 0.948 0.00959 0.929 0.967
24 432 15 0.917 0.01214 0.894 0.941
36 379 18 0.878 0.01477 0.849 0.907
48 323 13 0.846 0.01667 0.814 0.879
60 278 10 0.818 0.01828 0.783 0.855
120 74 43 0.628 0.03052 0.571 0.691
## Univariable Cox Proportional Hazard Model for: Tx_YN
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ Tx_YN, data = data)
n= 637, number of events= 169
(38 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
Tx_YNTRUE -1.4226 0.2411 0.1950 -7.296 2.97e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
Tx_YNTRUE 0.2411 4.148 0.1645 0.3533
Concordance= 0.595 (se = 0.012 )
Rsquare= 0.062 (max possible= 0.953 )
Likelihood ratio test= 40.63 on 1 df, p=1.841e-10
Wald test = 53.23 on 1 df, p=2.973e-13
Score (logrank) test = 62.55 on 1 df, p=2.554e-15
## Unadjusted Kaplan Meier Overall Survival Curve for: Tx_YN
Metastases at Dx
uni_var(test_var = "mets_at_dx_F", data_imp = data)_________________________________________________
## mets_at_dx_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ mets_at_dx_F, data = data)
n events median 0.95LCL 0.95UCL
mets_at_dx_F=FALSE 660 170 153.1 134.1 NA
mets_at_dx_F=TRUE 15 10 21.6 12.2 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ mets_at_dx_F, data = data)
mets_at_dx_F=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 554 42 0.933 0.00996 0.914 0.953
24 491 20 0.898 0.01231 0.874 0.922
36 424 22 0.856 0.01467 0.827 0.885
48 358 19 0.815 0.01665 0.783 0.849
60 309 13 0.784 0.01814 0.749 0.820
120 79 46 0.605 0.02845 0.552 0.663
mets_at_dx_F=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 10 4 0.718 0.120 0.5177 0.996
24 6 3 0.485 0.138 0.2778 0.845
36 3 3 0.242 0.120 0.0914 0.642
48 1 0 0.242 0.120 0.0914 0.642
60 1 0 0.242 0.120 0.0914 0.642
## Univariable Cox Proportional Hazard Model for: mets_at_dx_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ mets_at_dx_F, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
mets_at_dx_FTRUE 1.901 6.694 0.332 5.727 1.02e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
mets_at_dx_FTRUE 6.694 0.1494 3.492 12.83
Concordance= 0.531 (se = 0.005 )
Rsquare= 0.03 (max possible= 0.956 )
Likelihood ratio test= 20.3 on 1 df, p=6.622e-06
Wald test = 32.8 on 1 df, p=1.023e-08
Score (logrank) test = 43.91 on 1 df, p=3.435e-11
## Unadjusted Kaplan Meier Overall Survival Curve for: mets_at_dx_F
Tumor Size
uni_var(test_var = "T_SIZE", data_imp = data)_________________________________________________
## T_SIZE
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ T_SIZE, data = data)
n events median 0.95LCL 0.95UCL
T_SIZE=No Tumor 4 1 NA 12.58 NA
T_SIZE=Microscopic focus 17 3 NA 70.54 NA
T_SIZE=< 1 cm 84 21 NA 107.93 NA
T_SIZE=1-2 cm 91 16 136.8 136.77 NA
T_SIZE=2-3 cm 53 17 127.7 77.34 NA
T_SIZE=3-4 cm 27 12 83.6 46.95 NA
T_SIZE=4-5 cm 11 6 69.7 7.52 NA
T_SIZE=5-6 cm 8 4 116.2 21.65 NA
T_SIZE=>6 cm 31 14 88.7 35.81 NA
T_SIZE=NA_unk 349 86 153.1 136.97 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ T_SIZE, data = data)
T_SIZE=No Tumor
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 0 1.000 0.000 1.0 1
24 2 1 0.667 0.272 0.3 1
36 1 0 0.667 0.272 0.3 1
48 1 0 0.667 0.272 0.3 1
60 1 0 0.667 0.272 0.3 1
120 1 0 0.667 0.272 0.3 1
T_SIZE=Microscopic focus
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 14 1 0.941 0.0571 0.836 1
24 13 0 0.941 0.0571 0.836 1
36 12 0 0.941 0.0571 0.836 1
48 11 0 0.941 0.0571 0.836 1
60 8 0 0.941 0.0571 0.836 1
120 1 2 0.686 0.1608 0.434 1
T_SIZE=< 1 cm
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 76 4 0.951 0.0237 0.906 0.999
24 69 1 0.938 0.0268 0.887 0.992
36 60 4 0.881 0.0374 0.811 0.958
48 48 3 0.836 0.0436 0.755 0.926
60 40 4 0.765 0.0525 0.669 0.875
120 16 5 0.623 0.0720 0.497 0.782
T_SIZE=1-2 cm
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 83 2 0.977 0.0161 0.946 1.000
24 71 3 0.939 0.0263 0.889 0.992
36 62 2 0.911 0.0321 0.851 0.977
48 59 0 0.911 0.0321 0.851 0.977
60 49 1 0.896 0.0351 0.830 0.968
120 12 7 0.727 0.0659 0.608 0.868
T_SIZE=2-3 cm
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 40 5 0.899 0.0430 0.818 0.987
24 36 2 0.853 0.0517 0.757 0.960
36 32 1 0.827 0.0563 0.724 0.945
48 30 1 0.800 0.0604 0.690 0.928
60 28 0 0.800 0.0604 0.690 0.928
120 6 6 0.554 0.0955 0.395 0.777
T_SIZE=3-4 cm
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 22 5 0.815 0.0748 0.681 0.975
24 16 2 0.735 0.0864 0.583 0.925
36 14 1 0.689 0.0924 0.529 0.896
48 12 1 0.636 0.0994 0.468 0.864
60 10 1 0.583 0.1042 0.410 0.827
T_SIZE=4-5 cm
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 4 0.614 0.153 0.377 0.999
24 6 0 0.614 0.153 0.377 0.999
36 5 0 0.614 0.153 0.377 0.999
48 5 0 0.614 0.153 0.377 0.999
60 4 0 0.614 0.153 0.377 0.999
T_SIZE=5-6 cm
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 1 0.875 0.117 0.6734 1
24 3 1 0.656 0.209 0.3518 1
36 3 0 0.656 0.209 0.3518 1
48 2 0 0.656 0.209 0.3518 1
60 2 0 0.656 0.209 0.3518 1
120 1 1 0.328 0.254 0.0718 1
T_SIZE=>6 cm
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 27 3 0.901 0.0543 0.801 1.000
24 21 3 0.795 0.0748 0.661 0.956
36 15 4 0.632 0.0942 0.472 0.846
48 8 2 0.538 0.1010 0.372 0.777
60 8 0 0.538 0.1010 0.372 0.777
T_SIZE=NA_unk
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 286 21 0.936 0.0135 0.910 0.963
24 260 10 0.902 0.0167 0.870 0.936
36 223 13 0.856 0.0203 0.817 0.896
48 183 12 0.807 0.0236 0.762 0.854
60 160 7 0.773 0.0257 0.725 0.826
120 42 19 0.627 0.0384 0.556 0.707
## Univariable Cox Proportional Hazard Model for: T_SIZE
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ T_SIZE, data = data)
n= 675, number of events= 180
coef exp(coef) se(coef) z Pr(>|z|)
T_SIZEMicroscopic focus -0.59141 0.55354 1.15574 -0.512 0.609
T_SIZE< 1 cm -0.46595 0.62754 1.02471 -0.455 0.649
T_SIZE1-2 cm -0.70358 0.49481 1.03152 -0.682 0.495
T_SIZE2-3 cm -0.03174 0.96876 1.02979 -0.031 0.975
T_SIZE3-4 cm 0.61253 1.84509 1.04229 0.588 0.557
T_SIZE4-5 cm 0.98053 2.66586 1.08187 0.906 0.365
T_SIZE5-6 cm 0.63561 1.88818 1.11838 0.568 0.570
T_SIZE>6 cm 0.64804 1.91179 1.03634 0.625 0.532
T_SIZENA_unk -0.31738 0.72806 1.00654 -0.315 0.753
exp(coef) exp(-coef) lower .95 upper .95
T_SIZEMicroscopic focus 0.5535 1.8065 0.05746 5.332
T_SIZE< 1 cm 0.6275 1.5935 0.08422 4.676
T_SIZE1-2 cm 0.4948 2.0210 0.06553 3.737
T_SIZE2-3 cm 0.9688 1.0322 0.12872 7.291
T_SIZE3-4 cm 1.8451 0.5420 0.23923 14.230
T_SIZE4-5 cm 2.6659 0.3751 0.31985 22.219
T_SIZE5-6 cm 1.8882 0.5296 0.21090 16.905
T_SIZE>6 cm 1.9118 0.5231 0.25079 14.574
T_SIZENA_unk 0.7281 1.3735 0.10125 5.235
Concordance= 0.6 (se = 0.022 )
Rsquare= 0.044 (max possible= 0.956 )
Likelihood ratio test= 30.07 on 9 df, p=0.0004276
Wald test = 35.44 on 9 df, p=4.986e-05
Score (logrank) test = 39.19 on 9 df, p=1.064e-05Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: T_SIZE
Tumor specific Variables
Node Size
Cox Proportional Hazard Ratio
Model #1
Full analysis
model_one %>% summary()
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + CROWFLY + DX_STAGING_PROC_DAYS +
CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN + AGE_F +
SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + EDUCATION_F +
YEAR_OF_DIAGNOSIS, data = data)
n= 456, number of events= 129
(219 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
FACILITY_TYPE_FComprehensive Comm Ca Program 1.370e-01 1.147e+00 3.479e-01 0.394 0.693750
FACILITY_TYPE_FAcademic/Research Program 4.250e-02 1.043e+00 3.723e-01 0.114 0.909128
FACILITY_TYPE_FIntegrated Network Ca Program 1.010e-01 1.106e+00 3.903e-01 0.259 0.795835
FACILITY_LOCATION_FMiddle Atlantic 1.806e+00 6.087e+00 7.103e-01 2.543 0.011003 *
FACILITY_LOCATION_FSouth Atlantic 2.093e+00 8.106e+00 6.661e-01 3.142 0.001679 **
FACILITY_LOCATION_FEast North Central 2.309e+00 1.006e+01 6.626e-01 3.484 0.000494 ***
FACILITY_LOCATION_FEast South Central 2.369e+00 1.069e+01 7.032e-01 3.369 0.000754 ***
FACILITY_LOCATION_FWest North Central 2.274e+00 9.715e+00 6.951e-01 3.271 0.001071 **
FACILITY_LOCATION_FWest South Central 2.978e+00 1.966e+01 6.893e-01 4.321 1.55e-05 ***
FACILITY_LOCATION_FMountain 2.908e+00 1.832e+01 8.031e-01 3.621 0.000293 ***
FACILITY_LOCATION_FPacific 3.059e+00 2.130e+01 8.100e-01 3.776 0.000159 ***
CROWFLY -1.014e-03 9.990e-01 1.517e-03 -0.668 0.503954
DX_STAGING_PROC_DAYS -5.706e-03 9.943e-01 1.145e-02 -0.498 0.618404
CHEMO_YNYes 1.815e+00 6.139e+00 3.409e-01 5.322 1.02e-07 ***
CHEMO_YNUkn 1.744e-01 1.190e+00 5.398e-01 0.323 0.746700
RADIATION_YNYes -2.055e-01 8.143e-01 2.411e-01 -0.852 0.394097
SURGERY_YNUkn 8.573e-01 2.357e+00 8.045e-01 1.066 0.286601
SURGERY_YNYes -2.134e+00 1.184e-01 2.514e-01 -8.489 < 2e-16 ***
IMMUNO_YNYes -1.580e+01 1.377e-07 2.476e+03 -0.006 0.994910
IMMUNO_YNUkn 5.508e-01 1.735e+00 1.190e+00 0.463 0.643396
AGE_F(54,64] 2.704e-01 1.311e+00 4.189e-01 0.646 0.518556
AGE_F(64,74] 1.100e+00 3.005e+00 4.532e-01 2.428 0.015180 *
AGE_F(74,100] 1.891e+00 6.623e+00 4.748e-01 3.982 6.84e-05 ***
SEX_FFemale 4.116e-01 1.509e+00 8.133e-01 0.506 0.612768
RACE_FBlack -7.280e-02 9.298e-01 3.458e-01 -0.211 0.833265
RACE_FOther/Unk -8.538e-01 4.258e-01 8.992e-01 -0.950 0.342354
RACE_FAsian -1.591e+00 2.038e-01 1.163e+00 -1.368 0.171263
HISPANICYes -1.271e+00 2.807e-01 7.848e-01 -1.619 0.105431
HISPANICUnknown -7.433e-01 4.755e-01 3.884e-01 -1.914 0.055658 .
INSURANCE_FNone 2.439e+00 1.146e+01 5.940e-01 4.106 4.03e-05 ***
INSURANCE_FMedicaid 8.471e-01 2.333e+00 6.489e-01 1.305 0.191756
INSURANCE_FMedicare 7.426e-01 2.101e+00 3.441e-01 2.158 0.030895 *
INSURANCE_FOther Government 8.858e-01 2.425e+00 1.198e+00 0.740 0.459488
INSURANCE_FUnknown 2.792e-01 1.322e+00 6.895e-01 0.405 0.685581
INCOME_F$38,000 - $47,999 -9.580e-02 9.086e-01 3.518e-01 -0.272 0.785414
INCOME_F$48,000 - $62,999 -4.004e-01 6.700e-01 3.522e-01 -1.137 0.255613
INCOME_F$63,000 + 5.546e-02 1.057e+00 4.313e-01 0.129 0.897686
EDUCATION_F13 - 20.9% 4.014e-01 1.494e+00 3.532e-01 1.136 0.255830
EDUCATION_F7 - 12.9% 2.774e-01 1.320e+00 4.161e-01 0.667 0.504966
EDUCATION_FLess than 7% -2.669e-01 7.657e-01 4.933e-01 -0.541 0.588382
YEAR_OF_DIAGNOSIS2005 -3.466e-01 7.071e-01 5.298e-01 -0.654 0.513016
YEAR_OF_DIAGNOSIS2006 3.065e-01 1.359e+00 4.297e-01 0.713 0.475762
YEAR_OF_DIAGNOSIS2007 3.427e-01 1.409e+00 4.611e-01 0.743 0.457298
YEAR_OF_DIAGNOSIS2008 5.513e-01 1.736e+00 5.295e-01 1.041 0.297769
YEAR_OF_DIAGNOSIS2009 -2.187e-01 8.036e-01 5.293e-01 -0.413 0.679515
YEAR_OF_DIAGNOSIS2010 -5.171e-01 5.963e-01 5.169e-01 -1.000 0.317112
YEAR_OF_DIAGNOSIS2011 -2.753e-01 7.593e-01 5.037e-01 -0.547 0.584621
YEAR_OF_DIAGNOSIS2012 -1.114e+00 3.282e-01 6.149e-01 -1.812 0.070018 .
YEAR_OF_DIAGNOSIS2013 2.602e-01 1.297e+00 5.368e-01 0.485 0.627877
YEAR_OF_DIAGNOSIS2014 -5.479e-01 5.782e-01 6.566e-01 -0.834 0.404054
YEAR_OF_DIAGNOSIS2015 -9.574e-02 9.087e-01 6.250e-01 -0.153 0.878264
YEAR_OF_DIAGNOSIS2016 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program 1.147e+00 8.720e-01 0.57991 2.2679
FACILITY_TYPE_FAcademic/Research Program 1.043e+00 9.584e-01 0.50297 2.1646
FACILITY_TYPE_FIntegrated Network Ca Program 1.106e+00 9.039e-01 0.51481 2.3772
FACILITY_LOCATION_FMiddle Atlantic 6.087e+00 1.643e-01 1.51265 24.4918
FACILITY_LOCATION_FSouth Atlantic 8.106e+00 1.234e-01 2.19715 29.9057
FACILITY_LOCATION_FEast North Central 1.006e+01 9.938e-02 2.74560 36.8750
FACILITY_LOCATION_FEast South Central 1.069e+01 9.356e-02 2.69361 42.4118
FACILITY_LOCATION_FWest North Central 9.715e+00 1.029e-01 2.48774 37.9420
FACILITY_LOCATION_FWest South Central 1.966e+01 5.087e-02 5.09128 75.8959
FACILITY_LOCATION_FMountain 1.832e+01 5.458e-02 3.79634 88.4394
FACILITY_LOCATION_FPacific 2.130e+01 4.696e-02 4.35371 104.1707
CROWFLY 9.990e-01 1.001e+00 0.99602 1.0020
DX_STAGING_PROC_DAYS 9.943e-01 1.006e+00 0.97224 1.0169
CHEMO_YNYes 6.139e+00 1.629e-01 3.14693 11.9759
CHEMO_YNUkn 1.190e+00 8.400e-01 0.41325 3.4296
RADIATION_YNYes 8.143e-01 1.228e+00 0.50764 1.3061
SURGERY_YNUkn 2.357e+00 4.243e-01 0.48698 11.4062
SURGERY_YNYes 1.184e-01 8.448e+00 0.07232 0.1937
IMMUNO_YNYes 1.377e-07 7.263e+06 0.00000 Inf
IMMUNO_YNUkn 1.735e+00 5.765e-01 0.16845 17.8638
AGE_F(54,64] 1.311e+00 7.630e-01 0.57660 2.9787
AGE_F(64,74] 3.005e+00 3.328e-01 1.23630 7.3046
AGE_F(74,100] 6.623e+00 1.510e-01 2.61157 16.7961
SEX_FFemale 1.509e+00 6.626e-01 0.30653 7.4316
RACE_FBlack 9.298e-01 1.076e+00 0.47208 1.8312
RACE_FOther/Unk 4.258e-01 2.349e+00 0.07308 2.4808
RACE_FAsian 2.038e-01 4.907e+00 0.02087 1.9899
HISPANICYes 2.807e-01 3.563e+00 0.06028 1.3067
HISPANICUnknown 4.755e-01 2.103e+00 0.22210 1.0181
INSURANCE_FNone 1.146e+01 8.726e-02 3.57741 36.7136
INSURANCE_FMedicaid 2.333e+00 4.286e-01 0.65392 8.3230
INSURANCE_FMedicare 2.101e+00 4.759e-01 1.07067 4.1245
INSURANCE_FOther Government 2.425e+00 4.124e-01 0.23193 25.3536
INSURANCE_FUnknown 1.322e+00 7.564e-01 0.34222 5.1071
INCOME_F$38,000 - $47,999 9.086e-01 1.101e+00 0.45594 1.8109
INCOME_F$48,000 - $62,999 6.700e-01 1.492e+00 0.33596 1.3363
INCOME_F$63,000 + 1.057e+00 9.460e-01 0.45389 2.4617
EDUCATION_F13 - 20.9% 1.494e+00 6.694e-01 0.74755 2.9854
EDUCATION_F7 - 12.9% 1.320e+00 7.577e-01 0.58381 2.9834
EDUCATION_FLess than 7% 7.657e-01 1.306e+00 0.29120 2.0134
YEAR_OF_DIAGNOSIS2005 7.071e-01 1.414e+00 0.25033 1.9974
YEAR_OF_DIAGNOSIS2006 1.359e+00 7.360e-01 0.58519 3.1542
YEAR_OF_DIAGNOSIS2007 1.409e+00 7.098e-01 0.57064 3.4780
YEAR_OF_DIAGNOSIS2008 1.736e+00 5.762e-01 0.61480 4.8993
YEAR_OF_DIAGNOSIS2009 8.036e-01 1.244e+00 0.28480 2.2675
YEAR_OF_DIAGNOSIS2010 5.963e-01 1.677e+00 0.21651 1.6421
YEAR_OF_DIAGNOSIS2011 7.593e-01 1.317e+00 0.28293 2.0378
YEAR_OF_DIAGNOSIS2012 3.282e-01 3.047e+00 0.09835 1.0954
YEAR_OF_DIAGNOSIS2013 1.297e+00 7.709e-01 0.45300 3.7145
YEAR_OF_DIAGNOSIS2014 5.782e-01 1.730e+00 0.15964 2.0940
YEAR_OF_DIAGNOSIS2015 9.087e-01 1.100e+00 0.26693 3.0935
YEAR_OF_DIAGNOSIS2016 NA NA NA NA
Concordance= 0.837 (se = 0.028 )
Rsquare= 0.36 (max possible= 0.956 )
Likelihood ratio test= 203.2 on 51 df, p=0
Wald test = 168.7 on 51 df, p=1.654e-14
Score (logrank) test = 225.4 on 51 df, p=0Summary of Model
Linear Regression
#only include rows with known treatment information, n = 82
data2 <- data %>% filter(SURGERY_YN != "Ukn" & RADIATION_YN != "Ukn"
& CHEMO_YN != "Ukn" & IMMUNO_YN != "Ukn")
# include only variables with data available for at least 75% cases
# from the following set of variables of interest:
## FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY +
## DX_STAGING_PROC_DAYS +
## CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
## AGE + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F +
## EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT + GRADE_F
length(which(is.na(data2$SITE_TEXT))) / nrow(data2)[1] 0.3349282# excluded the following in this analysis due to missing data:
# DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns), SITE_TEXT
fit_surv <- lm(DX_LASTCONTACT_DEATH_MONTHS ~
FACILITY_TYPE_F +
CHEMO_YN + SURGERY_YN +
AGE_F + INSURANCE_F + INCOME_F +
EDUCATION_F + YEAR_OF_DIAGNOSIS,
data = data2)
summary(fit_surv) # R^2 = 0.4207, p < 2.2e-16
Call:
lm(formula = DX_LASTCONTACT_DEATH_MONTHS ~ FACILITY_TYPE_F +
CHEMO_YN + SURGERY_YN + AGE_F + INSURANCE_F + INCOME_F +
EDUCATION_F + YEAR_OF_DIAGNOSIS, data = data2)
Residuals:
Min 1Q Median 3Q Max
-92.604 -18.865 2.862 22.396 86.519
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 80.398 8.531 9.424 < 2e-16 ***
FACILITY_TYPE_FComprehensive Comm Ca Program -9.538 4.294 -2.221 0.026732 *
FACILITY_TYPE_FAcademic/Research Program -8.646 4.585 -1.886 0.059832 .
FACILITY_TYPE_FIntegrated Network Ca Program -6.480 5.251 -1.234 0.217709
CHEMO_YNYes -13.503 4.794 -2.817 0.005019 **
SURGERY_YNYes 27.347 3.706 7.380 5.73e-13 ***
AGE_F(54,64] -9.364 4.198 -2.231 0.026102 *
AGE_F(64,74] -6.979 4.981 -1.401 0.161698
AGE_F(74,100] -20.884 5.144 -4.060 5.61e-05 ***
INSURANCE_FNone -16.287 8.152 -1.998 0.046207 *
INSURANCE_FMedicaid 1.686 6.533 0.258 0.796435
INSURANCE_FMedicare -3.798 4.120 -0.922 0.356913
INSURANCE_FOther Government -8.467 11.939 -0.709 0.478529
INSURANCE_FUnknown 13.544 10.481 1.292 0.196774
INCOME_F$38,000 - $47,999 -4.625 4.576 -1.011 0.312593
INCOME_F$48,000 - $62,999 -5.857 4.869 -1.203 0.229518
INCOME_F$63,000 + -10.180 5.423 -1.877 0.060982 .
EDUCATION_F13 - 20.9% -3.511 4.802 -0.731 0.464950
EDUCATION_F7 - 12.9% 10.095 5.161 1.956 0.050932 .
EDUCATION_FLess than 7% 15.440 5.991 2.577 0.010219 *
YEAR_OF_DIAGNOSIS2005 10.294 7.055 1.459 0.145087
YEAR_OF_DIAGNOSIS2006 -1.357 6.616 -0.205 0.837608
YEAR_OF_DIAGNOSIS2007 -3.403 6.804 -0.500 0.617179
YEAR_OF_DIAGNOSIS2008 -4.147 7.758 -0.535 0.593168
YEAR_OF_DIAGNOSIS2009 -18.090 6.879 -2.630 0.008783 **
YEAR_OF_DIAGNOSIS2010 -21.321 7.203 -2.960 0.003207 **
YEAR_OF_DIAGNOSIS2011 -26.654 6.912 -3.856 0.000128 ***
YEAR_OF_DIAGNOSIS2012 -36.324 7.120 -5.102 4.61e-07 ***
YEAR_OF_DIAGNOSIS2013 -52.461 6.898 -7.605 1.21e-13 ***
YEAR_OF_DIAGNOSIS2014 -57.082 7.358 -7.758 4.10e-14 ***
YEAR_OF_DIAGNOSIS2015 -62.495 6.939 -9.006 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 32.54 on 562 degrees of freedom
(34 observations deleted due to missingness)
Multiple R-squared: 0.4501, Adjusted R-squared: 0.4207
F-statistic: 15.33 on 30 and 562 DF, p-value: < 2.2e-16# the following variables were excluded to
# improve the R-squared of the regression (initially R^2 = 0.4106):
# HISPANIC, RACE_F, FACILITY_LOCATION_F, CROWFLY, RADIATION_YN, IMMUNO_YN, SEX_FPrediction Logistic Regression Models
Surgery
no_Ukns <- data2 %>%
droplevels() %>%
mutate(SURGERY_YN = as.logical(SURGERY_YN))
# excluded the following in this analysis due to missing data:
# DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns) + SITE_TEXT
fit_surg <- glm(SURG_TF ~
FACILITY_TYPE_F + FACILITY_LOCATION_F +
CHEMO_YN + RADIATION_YN + IMMUNO_YN +
AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F +
EDUCATION_F + YEAR_OF_DIAGNOSIS,
data = no_Ukns)
summary(fit_surg)
Call:
glm(formula = SURG_TF ~ FACILITY_TYPE_F + FACILITY_LOCATION_F +
CHEMO_YN + RADIATION_YN + IMMUNO_YN + AGE_F + SEX_F + RACE_F +
HISPANIC + INSURANCE_F + INCOME_F + EDUCATION_F + YEAR_OF_DIAGNOSIS,
data = no_Ukns)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.00845 -0.05292 0.10994 0.21927 0.55376
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.651660 0.145713 4.472 9.42e-06 ***
FACILITY_TYPE_FComprehensive Comm Ca Program 0.022366 0.049984 0.447 0.654716
FACILITY_TYPE_FAcademic/Research Program 0.005020 0.053528 0.094 0.925311
FACILITY_TYPE_FIntegrated Network Ca Program 0.042396 0.060626 0.699 0.484654
FACILITY_LOCATION_FMiddle Atlantic 0.121849 0.082802 1.472 0.141710
FACILITY_LOCATION_FSouth Atlantic 0.236085 0.078723 2.999 0.002832 **
FACILITY_LOCATION_FEast North Central 0.233113 0.079585 2.929 0.003541 **
FACILITY_LOCATION_FEast South Central 0.246452 0.094099 2.619 0.009061 **
FACILITY_LOCATION_FWest North Central 0.226257 0.089148 2.538 0.011426 *
FACILITY_LOCATION_FWest South Central 0.160957 0.088551 1.818 0.069661 .
FACILITY_LOCATION_FMountain 0.246836 0.099376 2.484 0.013295 *
FACILITY_LOCATION_FPacific 0.359396 0.095469 3.765 0.000185 ***
CHEMO_YNYes -0.167294 0.056832 -2.944 0.003381 **
RADIATION_YNYes 0.084956 0.034938 2.432 0.015351 *
IMMUNO_YNYes 0.486867 0.180276 2.701 0.007134 **
AGE_F(54,64] -0.083242 0.047053 -1.769 0.077436 .
AGE_F(64,74] 0.010667 0.056472 0.189 0.850243
AGE_F(74,100] -0.125091 0.058317 -2.145 0.032390 *
SEX_FFemale -0.007287 0.095848 -0.076 0.939424
RACE_FBlack -0.105561 0.053772 -1.963 0.050137 .
RACE_FOther/Unk -0.145030 0.114562 -1.266 0.206070
RACE_FAsian -0.052305 0.117234 -0.446 0.655660
HISPANICYes -0.075258 0.091294 -0.824 0.410103
HISPANICUnknown 0.039973 0.056558 0.707 0.480018
INSURANCE_FNone -0.265593 0.093010 -2.856 0.004459 **
INSURANCE_FMedicaid -0.103641 0.076063 -1.363 0.173579
INSURANCE_FMedicare -0.066688 0.046605 -1.431 0.153020
INSURANCE_FOther Government 0.074204 0.135694 0.547 0.584708
INSURANCE_FUnknown -0.275175 0.118496 -2.322 0.020587 *
INCOME_F$38,000 - $47,999 0.012457 0.051984 0.240 0.810701
INCOME_F$48,000 - $62,999 -0.025082 0.055799 -0.450 0.653240
INCOME_F$63,000 + 0.021385 0.062719 0.341 0.733257
EDUCATION_F13 - 20.9% 0.034352 0.055219 0.622 0.534138
EDUCATION_F7 - 12.9% 0.073091 0.060256 1.213 0.225649
EDUCATION_FLess than 7% 0.048306 0.070943 0.681 0.496217
YEAR_OF_DIAGNOSIS2005 0.091067 0.080623 1.130 0.259160
YEAR_OF_DIAGNOSIS2006 0.111026 0.075344 1.474 0.141171
YEAR_OF_DIAGNOSIS2007 0.010534 0.076977 0.137 0.891205
YEAR_OF_DIAGNOSIS2008 0.030122 0.087763 0.343 0.731563
YEAR_OF_DIAGNOSIS2009 -0.026367 0.078985 -0.334 0.738642
YEAR_OF_DIAGNOSIS2010 -0.033775 0.082049 -0.412 0.680762
YEAR_OF_DIAGNOSIS2011 0.036652 0.078686 0.466 0.641544
YEAR_OF_DIAGNOSIS2012 0.022172 0.081462 0.272 0.785588
YEAR_OF_DIAGNOSIS2013 -0.007665 0.078903 -0.097 0.922648
YEAR_OF_DIAGNOSIS2014 -0.126214 0.084491 -1.494 0.135798
YEAR_OF_DIAGNOSIS2015 0.001531 0.078824 0.019 0.984506
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.1320378)
Null deviance: 85.761 on 592 degrees of freedom
Residual deviance: 72.225 on 547 degrees of freedom
(34 observations deleted due to missingness)
AIC: 528.35
Number of Fisher Scoring iterations: 2# the following variables were excluded to
# improve the R-squared of the regression (initially residual = 72.225):
# none
exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95))) Odds ratio 2.5 % 97.5 %
(Intercept) 1.9187228 1.4420519 2.5529575
FACILITY_TYPE_FComprehensive Comm Ca Program 1.0226181 0.9271862 1.1278725
FACILITY_TYPE_FAcademic/Research Program 1.0050330 0.9049345 1.1162036
FACILITY_TYPE_FIntegrated Network Ca Program 1.0433079 0.9264196 1.1749441
FACILITY_LOCATION_FMiddle Atlantic 1.1295840 0.9603676 1.3286163
FACILITY_LOCATION_FSouth Atlantic 1.2662821 1.0852297 1.4775401
FACILITY_LOCATION_FEast North Central 1.2625237 1.0801816 1.4756464
FACILITY_LOCATION_FEast South Central 1.2794778 1.0639858 1.5386141
FACILITY_LOCATION_FWest North Central 1.2538977 1.0528798 1.4932943
FACILITY_LOCATION_FWest South Central 1.1746348 0.9874791 1.3972619
FACILITY_LOCATION_FMountain 1.2799691 1.0534409 1.5552090
FACILITY_LOCATION_FPacific 1.4324644 1.1880112 1.7272180
CHEMO_YNYes 0.8459507 0.7567800 0.9456283
RADIATION_YNYes 1.0886690 1.0166155 1.1658294
IMMUNO_YNYes 1.6272099 1.1428588 2.3168323
AGE_F(54,64] 0.9201288 0.8390674 1.0090215
AGE_F(64,74] 1.0107245 0.9048236 1.1290201
AGE_F(74,100] 0.8824164 0.7871082 0.9892652
SEX_FFemale 0.9927393 0.8227151 1.1979010
RACE_FBlack 0.8998193 0.8098129 0.9998295
RACE_FOther/Unk 0.8649960 0.6910324 1.0827540
RACE_FAsian 0.9490395 0.7542141 1.1941914
HISPANICYes 0.9275041 0.7755429 1.1092407
HISPANICUnknown 1.0407826 0.9315745 1.1627932
INSURANCE_FNone 0.7667512 0.6389760 0.9200775
INSURANCE_FMedicaid 0.9015489 0.7766840 1.0464878
INSURANCE_FMedicare 0.9354871 0.8538232 1.0249617
INSURANCE_FOther Government 1.0770260 0.8255118 1.4051707
INSURANCE_FUnknown 0.7594389 0.6020447 0.9579812
INCOME_F$38,000 - $47,999 1.0125354 0.9144522 1.1211388
INCOME_F$48,000 - $62,999 0.9752298 0.8742007 1.0879346
INCOME_F$63,000 + 1.0216155 0.9034441 1.1552437
EDUCATION_F13 - 20.9% 1.0349484 0.9287870 1.1532442
EDUCATION_F7 - 12.9% 1.0758287 0.9559894 1.2106906
EDUCATION_FLess than 7% 1.0494917 0.9132550 1.2060519
YEAR_OF_DIAGNOSIS2005 1.0953429 0.9352419 1.2828510
YEAR_OF_DIAGNOSIS2006 1.1174237 0.9640167 1.2952428
YEAR_OF_DIAGNOSIS2007 1.0105894 0.8690647 1.1751611
YEAR_OF_DIAGNOSIS2008 1.0305805 0.8677169 1.2240122
YEAR_OF_DIAGNOSIS2009 0.9739776 0.8342892 1.1370546
YEAR_OF_DIAGNOSIS2010 0.9667889 0.8231729 1.1354612
YEAR_OF_DIAGNOSIS2011 1.0373321 0.8890779 1.2103079
YEAR_OF_DIAGNOSIS2012 1.0224197 0.8715428 1.1994157
YEAR_OF_DIAGNOSIS2013 0.9923644 0.8501758 1.1583334
YEAR_OF_DIAGNOSIS2014 0.8814260 0.7469082 1.0401704
YEAR_OF_DIAGNOSIS2015 1.0015326 0.8581640 1.1688530Metastasis at Time of Diagnosis
fit_mets <- glm(mets_at_dx_F ~
FACILITY_TYPE_F + FACILITY_LOCATION_F +
AGE_F + SEX_F + RACE_F + INSURANCE_F + INCOME_F +
EDUCATION_F + YEAR_OF_DIAGNOSIS,
data = data)
# the following variables were excluded to
# improve the R-squared of the regression (initial residual = 12.525):
# HISPANIC +
summary(fit_mets)
Call:
glm(formula = mets_at_dx_F ~ FACILITY_TYPE_F + FACILITY_LOCATION_F +
AGE_F + SEX_F + RACE_F + INSURANCE_F + INCOME_F + EDUCATION_F +
YEAR_OF_DIAGNOSIS, data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.18895 -0.04985 -0.01444 0.01144 0.96172
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.060588 0.057592 -1.052 0.29321
FACILITY_TYPE_FComprehensive Comm Ca Program -0.010098 0.019712 -0.512 0.60864
FACILITY_TYPE_FAcademic/Research Program 0.016207 0.021212 0.764 0.44516
FACILITY_TYPE_FIntegrated Network Ca Program -0.002913 0.023971 -0.122 0.90333
FACILITY_LOCATION_FMiddle Atlantic 0.036927 0.032567 1.134 0.25729
FACILITY_LOCATION_FSouth Atlantic 0.026926 0.031237 0.862 0.38905
FACILITY_LOCATION_FEast North Central 0.052368 0.031665 1.654 0.09869 .
FACILITY_LOCATION_FEast South Central 0.008855 0.037615 0.235 0.81397
FACILITY_LOCATION_FWest North Central 0.016227 0.035243 0.460 0.64537
FACILITY_LOCATION_FWest South Central 0.025065 0.034905 0.718 0.47299
FACILITY_LOCATION_FMountain 0.038322 0.039111 0.980 0.32757
FACILITY_LOCATION_FPacific 0.016309 0.037563 0.434 0.66432
AGE_F(54,64] -0.044541 0.018646 -2.389 0.01722 *
AGE_F(64,74] -0.042685 0.022147 -1.927 0.05441 .
AGE_F(74,100] -0.047581 0.022299 -2.134 0.03327 *
SEX_FFemale 0.027386 0.038086 0.719 0.47239
RACE_FBlack -0.003492 0.020659 -0.169 0.86584
RACE_FOther/Unk -0.016818 0.044897 -0.375 0.70810
RACE_FAsian 0.053944 0.047789 1.129 0.25944
INSURANCE_FNone 0.113028 0.037154 3.042 0.00245 **
INSURANCE_FMedicaid 0.084171 0.029490 2.854 0.00446 **
INSURANCE_FMedicare 0.033215 0.018440 1.801 0.07217 .
INSURANCE_FOther Government 0.014923 0.055474 0.269 0.78801
INSURANCE_FUnknown 0.075632 0.044625 1.695 0.09063 .
INCOME_F$38,000 - $47,999 0.018432 0.020708 0.890 0.37378
INCOME_F$48,000 - $62,999 0.033849 0.022261 1.521 0.12891
INCOME_F$63,000 + 0.045191 0.024936 1.812 0.07045 .
EDUCATION_F13 - 20.9% -0.009875 0.021661 -0.456 0.64865
EDUCATION_F7 - 12.9% -0.024820 0.023738 -1.046 0.29619
EDUCATION_FLess than 7% -0.021722 0.027644 -0.786 0.43230
YEAR_OF_DIAGNOSIS2005 0.003146 0.031296 0.101 0.91996
YEAR_OF_DIAGNOSIS2006 0.006157 0.029339 0.210 0.83386
YEAR_OF_DIAGNOSIS2007 0.015649 0.030588 0.512 0.60912
YEAR_OF_DIAGNOSIS2008 -0.001463 0.033809 -0.043 0.96550
YEAR_OF_DIAGNOSIS2009 0.002255 0.031048 0.073 0.94211
YEAR_OF_DIAGNOSIS2010 0.082894 0.031715 2.614 0.00918 **
YEAR_OF_DIAGNOSIS2011 0.032651 0.031033 1.052 0.29316
YEAR_OF_DIAGNOSIS2012 0.019965 0.032511 0.614 0.53939
YEAR_OF_DIAGNOSIS2013 0.073395 0.030823 2.381 0.01757 *
YEAR_OF_DIAGNOSIS2014 0.050970 0.033018 1.544 0.12319
YEAR_OF_DIAGNOSIS2015 0.044258 0.031312 1.413 0.15804
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.02228879)
Null deviance: 14.646 on 635 degrees of freedom
Residual deviance: 13.262 on 595 degrees of freedom
(39 observations deleted due to missingness)
AIC: -572.63
Number of Fisher Scoring iterations: 2exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_mets, level = 0.95))) Odds ratio 2.5 % 97.5 %
(Intercept) 0.9412105 0.8407448 1.0536814
FACILITY_TYPE_FComprehensive Comm Ca Program 0.9899525 0.9524354 1.0289475
FACILITY_TYPE_FAcademic/Research Program 1.0163388 0.9749501 1.0594845
FACILITY_TYPE_FIntegrated Network Ca Program 0.9970915 0.9513286 1.0450558
FACILITY_LOCATION_FMiddle Atlantic 1.0376176 0.9734567 1.1060073
FACILITY_LOCATION_FSouth Atlantic 1.0272913 0.9662840 1.0921503
FACILITY_LOCATION_FEast North Central 1.0537638 0.9903539 1.1212338
FACILITY_LOCATION_FEast South Central 1.0088944 0.9371900 1.0860848
FACILITY_LOCATION_FWest North Central 1.0163597 0.9485248 1.0890459
FACILITY_LOCATION_FWest South Central 1.0253814 0.9575774 1.0979866
FACILITY_LOCATION_FMountain 1.0390656 0.9623918 1.1218481
FACILITY_LOCATION_FPacific 1.0164430 0.9442977 1.0941003
AGE_F(54,64] 0.9564369 0.9221139 0.9920374
AGE_F(64,74] 0.9582128 0.9175084 1.0007230
AGE_F(74,100] 0.9535336 0.9127561 0.9961327
SEX_FFemale 1.0277644 0.9538375 1.1074210
RACE_FBlack 0.9965144 0.9569715 1.0376912
RACE_FOther/Unk 0.9833231 0.9004927 1.0737725
RACE_FAsian 1.0554253 0.9610571 1.1590596
INSURANCE_FNone 1.1196631 1.0410271 1.2042391
INSURANCE_FMedicaid 1.0878153 1.0267224 1.1525434
INSURANCE_FMedicare 1.0337732 0.9970773 1.0718197
INSURANCE_FOther Government 1.0150352 0.9104613 1.1316202
INSURANCE_FUnknown 1.0785659 0.9882384 1.1771495
INCOME_F$38,000 - $47,999 1.0186026 0.9780892 1.0607940
INCOME_F$48,000 - $62,999 1.0344282 0.9902651 1.0805610
INCOME_F$63,000 + 1.0462276 0.9963239 1.0986309
EDUCATION_F13 - 20.9% 0.9901741 0.9490168 1.0331163
EDUCATION_F7 - 12.9% 0.9754859 0.9311406 1.0219432
EDUCATION_FLess than 7% 0.9785120 0.9269061 1.0329911
YEAR_OF_DIAGNOSIS2005 1.0031509 0.9434671 1.0666103
YEAR_OF_DIAGNOSIS2006 1.0061756 0.9499500 1.0657290
YEAR_OF_DIAGNOSIS2007 1.0157718 0.9566641 1.0785315
YEAR_OF_DIAGNOSIS2008 0.9985383 0.9345160 1.0669467
YEAR_OF_DIAGNOSIS2009 1.0022579 0.9430870 1.0651413
YEAR_OF_DIAGNOSIS2010 1.0864268 1.0209510 1.1561018
YEAR_OF_DIAGNOSIS2011 1.0331900 0.9722211 1.0979823
YEAR_OF_DIAGNOSIS2012 1.0201654 0.9571879 1.0872865
YEAR_OF_DIAGNOSIS2013 1.0761555 1.0130669 1.1431730
YEAR_OF_DIAGNOSIS2014 1.0522913 0.9863506 1.1226404
YEAR_OF_DIAGNOSIS2015 1.0452525 0.9830345 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SX09GX0RJQUdOT1NJUyIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgo8IS0tICMjUHJpbWFyeSBTaXRlIC0tPgoKPCEtLSBgYGB7cn0gLS0+CjwhLS0gdW5pX3Zhcih0ZXN0X3ZhciA9ICJTSVRFX1RFWFQiLCBkYXRhX2ltcCA9IGRhdGEpIC0tPgo8IS0tIGBgYCAtLT4KCgojI0hpc3RvbG9neQoKYGBge3J9CiN1bmlfdmFyKHRlc3RfdmFyID0gIkhJU1RPTE9HWV9GX0xJTSIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgo8IS0tICMjQmVoYXZpb3IgLS0+Cgo8IS0tIGBgYHtyfSAtLT4KPCEtLSB1bmlfdmFyKHRlc3RfdmFyID0gIkJFSEFWSU9SIiwgZGF0YV9pbXAgPSBkYXRhKSAtLT4KPCEtLSBgYGAgLS0+CgojI0dyYWRlCgpgYGB7cn0KdW5pX3Zhcih0ZXN0X3ZhciA9ICJHUkFERV9GIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiMjQ2xpbmljYWwgVCBTdGFnZQoKYGBge3J9CiN1bmlfdmFyKHRlc3RfdmFyID0gIlROTV9DTElOX1QiLCBkYXRhX2ltcCA9IGRhdGEpCmBgYAoKIyNDbGluaWNhbCBOIFN0YWdlCgpgYGB7cn0KI3VuaV92YXIodGVzdF92YXIgPSAiVE5NX0NMSU5fTiIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgojI0NsaW5pY2FsIE0gU3RhZ2UKCmBgYHtyfQojdW5pX3Zhcih0ZXN0X3ZhciA9ICJUTk1fQ0xJTl9NIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiMjQ2xpbmljYWwgU3RhZ2UgR3JvdXAKCmBgYHtyfQp1bmlfdmFyKHRlc3RfdmFyID0gIlROTV9DTElOX1NUQUdFX0dST1VQIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiMjUGF0aG9sb2dpYyBUIFN0YWdlCgpgYGB7cn0KdW5pX3Zhcih0ZXN0X3ZhciA9ICJUTk1fUEFUSF9UIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiMjUGF0aG9sb2dpYyBOIFN0YWdlCgpgYGB7cn0KdW5pX3Zhcih0ZXN0X3ZhciA9ICJUTk1fUEFUSF9OIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiMjUGF0aG9sb2dpYyBNIFN0YWdlCgpgYGB7cn0KdW5pX3Zhcih0ZXN0X3ZhciA9ICJUTk1fUEFUSF9NIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiMjUGF0aG9sb2dpYyBTdGFnZSBHcm91cAoKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAiVE5NX1BBVEhfU1RBR0VfR1JPVVAiLCBkYXRhX2ltcCA9IGRhdGEpCmBgYAoKIyNNYXJnaW5zCmBgYHtyfQp1bmlfdmFyKHRlc3RfdmFyID0gIk1BUkdJTlMiLCBkYXRhX2ltcCA9IGRhdGEpCmBgYAoKIyNNYXJnaW5zIFllcy9ObwpgYGB7cn0KI3VuaV92YXIodGVzdF92YXIgPSAiTUFSR0lOU19ZTiIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgojIzMwIERheSBSZWFkbWlzc2lvbgoKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAiUkVBRE1fSE9TUF8zMF9EQVlTX0YiLCBkYXRhX2ltcCA9IGRhdGEpCmBgYAoKIyNSYWRpYXRpb24gVHlwZQoKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAiUlhfU1VNTV9SQURJQVRJT05fRiIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgoKIyNMeW1waG92YXNjdWxhciBJbnZhc2lvbgoKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAiTFlNUEhfVkFTQ1VMQVJfSU5WQVNJT05fRiIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgojI0VuZG9zY29waWMvUm9ib3RpYwoKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAiUlhfSE9TUF9TVVJHX0FQUFJfMjAxMF9GIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiMjU3VyZ2VyeSBSYWRpYXRpb24gU2VxdWVuY2UgCgpgYGB7cn0KdW5pX3Zhcih0ZXN0X3ZhciA9ICJTVVJHX1JBRF9TRVEiLCBkYXRhX2ltcCA9IGRhdGEpCmBgYAoKIyNTdXJnZXJ5IFllcy9ObwoKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAiU1VSR0VSWV9ZTiIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgojI1JhZGlhdGlvbiBZZXMvTm8KCmBgYHtyfQp1bmlfdmFyKHRlc3RfdmFyID0gIlJBRElBVElPTl9ZTiIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgojI0NoZW1vIFllcy9ObwoKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAiQ0hFTU9fWU4iLCBkYXRhX2ltcCA9IGRhdGEpCmBgYAoKCiMjVHJlYXRtZW50IFllcy9ObwpgYGB7cn0KdW5pX3Zhcih0ZXN0X3ZhciA9ICJUeF9ZTiIsIGRhdGFfaW1wID0gZGF0YSkKYGBgCgojI01ldGFzdGFzZXMgYXQgRHgKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAibWV0c19hdF9keF9GIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiMjVHVtb3IgU2l6ZQoKYGBge3J9CnVuaV92YXIodGVzdF92YXIgPSAiVF9TSVpFIiwgZGF0YV9pbXAgPSBkYXRhKQpgYGAKCiNUdW1vciBzcGVjaWZpYyBWYXJpYWJsZXMKCgojIyNOb2RlIFNpemUKCgojQ294IFByb3BvcnRpb25hbCBIYXphcmQgUmF0aW8KCiMjTW9kZWwgIzEKCiMjI0Z1bGwgYW5hbHlzaXMKCmBgYHtyfQptb2RlbF9vbmUgPC0gY294cGgoU3VydihEWF9MQVNUQ09OVEFDVF9ERUFUSF9NT05USFMsIFBVRl9WSVRBTF9TVEFUVVMgPT0gMCkKICAgICAgICAgICAgICAgICAgICAgfiBGQUNJTElUWV9UWVBFX0YgKyBGQUNJTElUWV9MT0NBVElPTl9GICsgQ1JPV0ZMWSArIAogICAgICAgICAgICAgICAgIERYX1NUQUdJTkdfUFJPQ19EQVlTICsgCiAgICAgICAgICAgICAgICAgQ0hFTU9fWU4gKyBSQURJQVRJT05fWU4gKyBTVVJHRVJZX1lOICsgSU1NVU5PX1lOICsKICAgICAgICAgICAgICAgICBBR0VfRiArIFNFWF9GICsgUkFDRV9GICsgSElTUEFOSUMgKyBJTlNVUkFOQ0VfRiArIElOQ09NRV9GICsgCiAgICAgICAgICAgICAgICAgRURVQ0FUSU9OX0YgKyBZRUFSX09GX0RJQUdOT1NJUywKICAgICAgICAgICAgICAgICAgICAgZGF0YSA9IGRhdGEpCm1vZGVsX29uZSAlPiUgc3VtbWFyeSgpCgoKYGBgCgojIyNTdW1tYXJ5IG9mIE1vZGVsCgpgYGB7cn0KbW9kZWxfb25lICU+JQogICAgICAgIHRpZHkoLiwgZXhwb25lbnRpYXRlID0gVFJVRSkgJT4lCiAgICAgICAgc2VsZWN0KHRlcm0sIGVzdGltYXRlLCBjb25mLmxvdywgY29uZi5oaWdoLCBwLnZhbHVlKSAlPiUKICAgICAgICByZW5hbWUoVmFyaWFibGUgPSB0ZXJtLAogICAgICAgICAgICAgICBIYXphcmRfUmF0aW8gPSBlc3RpbWF0ZSkgJT4lCiAgICAgICAgdGJsX2RmICU+JQogICAgICAgIHByaW50KG4gPSBucm93KC4pKQoKYGBgCgojIyBMaW5lYXIgUmVncmVzc2lvbiAKYGBge3J9Cgojb25seSBpbmNsdWRlIHJvd3Mgd2l0aCBrbm93biB0cmVhdG1lbnQgaW5mb3JtYXRpb24sIG4gPSA4MgpkYXRhMiA8LSBkYXRhICU+JSBmaWx0ZXIoU1VSR0VSWV9ZTiAhPSAiVWtuIiAmIFJBRElBVElPTl9ZTiAhPSAiVWtuIgogICAgICAgICAgICAgICAgICAgICAgICAgJiBDSEVNT19ZTiAhPSAiVWtuIiAmIElNTVVOT19ZTiAhPSAiVWtuIikKCiMgaW5jbHVkZSBvbmx5IHZhcmlhYmxlcyB3aXRoIGRhdGEgYXZhaWxhYmxlIGZvciBhdCBsZWFzdCA3NSUgY2FzZXMgCiMgZnJvbSB0aGUgZm9sbG93aW5nIHNldCBvZiB2YXJpYWJsZXMgb2YgaW50ZXJlc3Q6CgojIyBGQUNJTElUWV9UWVBFX0YgKyBGQUNJTElUWV9HRU9HUkFQSFkgKyBDUk9XRkxZICsgCiMjICAgICAgICAgICAgICAgICBEWF9TVEFHSU5HX1BST0NfREFZUyArIAojIyAgICAgICAgICAgICAgICAgQ0hFTU9fWU4gKyBSQURJQVRJT05fWU4gKyBTVVJHRVJZX1lOICsgSU1NVU5PX1lOICsKIyMgICAgICAgICAgICAgICAgIEFHRSArIFNFWF9GICsgUkFDRV9GICsgSElTUEFOSUMgKyBJTlNVUkFOQ0VfRiArIElOQ09NRV9GICsgCiMjICAgICAgICAgICAgICAgICBFRFVDQVRJT05fRiArIFlFQVJfT0ZfRElBR05PU0lTICsgU0lURV9URVhUICsgR1JBREVfRgoKbGVuZ3RoKHdoaWNoKGlzLm5hKGRhdGEyJFNJVEVfVEVYVCk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